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1
Woro Tri Handayani, Nandia Rani, Martinus Maslim, and Paulus Mudjihartono. "Forecasting of Catfish Sales by Time Series Using the SARIMA method." Jurnal Buana Informatika 11, no. 2 (October 31, 2020): 83. http://dx.doi.org/10.24002/jbi.v11i2.3535.
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Abstrak. Sistem informasi yang mengotomatiskan proses bisnis, terutama dengan persyaratan khusus masih relevan saat ini. Clarias Makmur, sebuah usaha mikro di Indonesia yang membiakkan dan menjual ikan lele menggunakan sistem informasi ini untuk menjalankan penjualan, pengeluaran, modal, dan pelaporan mereka. Penjualan ikan lele sebagai makhluk hidup memiliki ciri khas tersendiri yang menunjukkan pola musiman yang unik. Sebuah model bernama SARIMA (Seasonal Autoregressive Integrated Moving Average) kemudian diusulkan untuk memprediksi penjualan. Lebih lanjut, sistem yang disebut SITRAN dibuat online supaya perusahaan mikro ini beroperasi secara fleksibel. Ada 400 data penjualan yang digunakan dalam metode ini untuk membuat model dan prediksi, sedangkan 100 data lainnya digunakan untuk menguji akurasi metode. Hasil penelitian menunjukkan bahwa SARIMAX (21,2,0) (1,0,0,12) adalah model terbaik yang ditemukan dalam percobaan dan yang memberikan RMSE terkecil. Kata kunci: prediksi; penjualan ikan lele; pola musiman; SARIMA Abstract. An information system that automates a business process, especially with specific requirement is still relevant these days. Clarias Makmur, a micro cooperation in Indonesia that breeds and sells catfish uses such an information system to carry out their sales, expenses, capital and reporting. The sales of catfish as a living creature have their own characteristics showing the unique seasonal pattern. A model named SARIMA (Seasonal Autoregressive Integrated Moving Average) is then proposed to predict the sales. Furthermore the system called SITRAN is made to be online for the cooperation to operate flexibly. There are 400 sales data used for the method to model and predict, while another 100 are used to test the method accuracy. The result shows that SARIMAX(21,2,0)(1,0,0,12) is the best model found in the experiment giving the smallest RMSE. Keywords: prediction; catfish sales; seasonal pattern; SARIMA
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Permatasari, Novia. "Penggunaan Indeks Google Trend Dalam Peramalan Jumlah Pengunjung Taman Rekreasi Selecta Tahun 2020." Seminar Nasional Official Statistics 2021, no. 1 (November 1, 2021): 1019–24. http://dx.doi.org/10.34123/semnasoffstat.v2021i1.993.
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Kota Batu merupakan salah satu daerah potensi pariwisata di Indonesia, dengan salah satu tujuan pariwisata andalan adalah Taman Rekreasi Selecta. Sejak tahun 2016 hingga 2019, Taman Rekreasi Selecta secara konsisten menjadi tempat wisata dengan jumlah pengunjung terbanyak di Kota Batu. Publikasi data kunjungan wisatawan yang hanya dilakukan sekali dalam satu tahun menunjukkan adanya selang waktu antara pengumpulan dan publikasi data, sehingga pemanfaatan data kunjungan wisatawan tersebut kurang maksimal. Permasalahan tersebut dapat diatasi dengan memanfaatkan real-tima data, yaitu big data. Pada penelitian ini, peneliti akan melakukan nowcasting jumlah pengunjung Taman Rekreasi Selecta pada tahun 2020, serta menggunakan Indeks Google Trend (IGT) yang diharapkan mampu meningkatkan akurasi hasil prediksi. Metode peramalan data runtun waktu yang digunakan adalah SARIMA dan SARIMAX dengan IGT sebagai variabel penjelas. Dibandingkan dengan model SARIMA, metode SARIMAX dengan IGT mampu menurunkan nilai MAE data out-sample hingga 32% dan MAPE sebesar 28%. Perbandingan kedua metode juga menunjukkan bahwa hasil peramalan menggunakan SARIMAX dengan IGT lebih mampu menangkap volatilitas data runtun waktu dari pada model SARIMA. Hasil peramalan jumlah pengunjung Selecta tahun 2020 menunjukkan sempat terjadinya penurunan jumlah pengunjung akibat pandemi covid-19 dan kembali naik diakhir tahun 2020.
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Rochayati, Isti, Utami Dyah Syafitri, I. Made Sumertajaya, and Indonesian Journal of Statistics and Its Applications IJSA. "KAJIAN MODEL PERAMALAN KUNJUNGAN WISATAWAN MANCANEGARA DI BANDARA KUALANAMU MEDAN TANPA DAN DENGAN KOVARIAT." Indonesian Journal of Statistics and Its Applications 3, no. 1 (February 28, 2019): 18–32. http://dx.doi.org/10.29244/ijsa.v3i1.171.
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Foreign tourist arrivals could be considered as time series data. Modelling these data could make use of internal and external factors. The techniques employed here to model these time series data are SARIMA, SARIMAX, VARIMA, and VARIMAX. SARIMA is a model for seasonal data and VARIMA is a model for multivariate time series data. If some explanatory variables are incorporated and have significant influence on the response, the former two models become SARIMAX and VARIMAX respectively. Three stages of creating the model are model identification, parameter estimation, and model diagnostics. The variables used in this study were foreign tourist visits, international passenger arrivals, inflation rates, currency exchange rates, and Gross Regional Domestic Product (GRDP) over the period of 2010-2017. All four models fulfill their model assumptions and therefore could be applied. The best model of foreign tourist arrivals was VARIMA with the value of MAPE testing data = 6.123.
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Amelia, Ririn, Elyas Kustiawan, Ineu Sulistiana, and Desy Yuliana Dalimunthe. "FORECASTING RAINFALL IN PANGKALPINANG CITY USING SEASONAL AUTOREGRESSIVE INTEGRATED MOVING AVERAGE WITH EXOGENOUS (SARIMAX)." BAREKENG: Jurnal Ilmu Matematika dan Terapan 16, no. 1 (March 21, 2022): 137–46. http://dx.doi.org/10.30598/barekengvol16iss1pp137-146.
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Changes in extreme rainfall can cause disasters or losses for the wider community, so information about future rainfall is also needed. Rainfall is included in the category of time series data. One of the time series methods that can be used is Autoregressive Integrated Moving Average (ARIMA) or Seasonal ARIMA (SARIMA). However, this model only involves one variable without involving its dependence on other variables. One of the factors that can affect rainfall is wind speed which can affect the formation of convective clouds. In this study, the ARIMA model was expanded by adding eXogen variables and seasonal elements, namely the SARIMAX model (Seasonal ARIMA with eXogenous input). Based on the analysis that has been carried out, the prediction of rainfall in Pangkalpinang City, Bangka Belitung Islands Province can be modeled with the SARIMAX model (0,1,3)(0,1,1){12} for monthly rainfall and SARIMAX (0,1,2 )(0,1,3){12} for maximum daily rainfall. When compared with the actual data and previous studies using ARIMAX, the SARIMAX model is still better in the forecasting process when compared to the ARIMAX model. If viewed based on the AIC value of the SARIMA model, the SARIMAX model is also more suitable to be used to predict rainfall in Pangkalpinang City.
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Chutiman, Nipaporn, Pannarat Guayjarernpanishk, Monchaya Chiangpradit, Piyapatr Busababodhin, Saowanee Rattanawan, and Butsakorn Kong-Led. "The Forecasting Model with Climate Variables of the Re-emerging Disease Rate in Elderly Patients." WSEAS TRANSACTIONS ON ENVIRONMENT AND DEVELOPMENT 17 (August 4, 2021): 866–75. http://dx.doi.org/10.37394/232015.2021.17.81.
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This research forecasted the incidence rate per 100,000 elderly population with food poisoning, pneumonia, and fever of unknown origin in Khon Kaen Province and Roi Et Province in the northeastern part of Thailand. In the study, the time series forecasting with Box-Jenkins Method (SARIMA model) and Box-Jenkins Method with climate variables, i.e total monthly rainfall, maximum average monthly temperature, average relative humidity, minimum average monthly temperature and average temperature (SARIMAX model) was performed. The study results revealed that the forecasting accuracy was closely similar to the model without the climate variables in the combined analysis although such climate variables had relationship with the incidence rate per 100,000 elderly population with food poisoning, pneumonia, and fever of unknown origin. Therefore, the appropriate model should be the SARIMA model because it is easier for analysis but with higher forecasting accuracy than the SARIMAX model.
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Faris Nasirudin and Abdullah Ahmad dzikrullah. "Pemodelan Harga Cabai Indonesia dengan Metode Seasonal ARIMAX." Jurnal Statistika dan Aplikasinya 7, no. 1 (June 30, 2023): 105–15. http://dx.doi.org/10.21009/jsa.07110.
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Chili is one of the plants favored by the people of Indonesia because Indonesian cuisine is famous for its spicy taste and spices in every food dish. The rise and fall of chili prices in the market are caused by chili farmers whose production decision-making processes are allegedly not handled and supported by a good production and price forecast. Therefore, analysis is needed to see the forecasting of chili prices in Indonesia in the future. The method that researchers use in forecasting in this study is the SARIMA and SARIMAX methods using the variables of rainfall, inflation, and google trend. The analysis shows that the SARIMAX method is the best model for predicting chili prices with a MAPE value of 6.889% compared to a MAPE SARIMA value of 7.630%.
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Wang, H., C. W. Tian, W. M. Wang, and X. M. Luo. "Time-series analysis of tuberculosis from 2005 to 2017 in China." Epidemiology and Infection 146, no. 8 (April 30, 2018): 935–39. http://dx.doi.org/10.1017/s0950268818001115.
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AbstractSeasonal autoregressive integrated moving average (SARIMA) has been used to model nationwide tuberculosis (TB) incidence in other countries. This study aimed to characterise monthly TB notification rate in China. Monthly TB notification rate from 2005 to 2017 was used. Time-series analysis was based on a SARIMA model and a hybrid model of SARIMA-generalised regression neural network (GRNN) model. A decreasing trend (3.17% per years, P < 0.01) and seasonal variation of TB notification rate were found from 2005 to 2016 in China, with a predominant peak in spring. A SARIMA model of ARIMA (0,1,1) (0,1,1)12 was identified. The mean error rate of the single SARIMA model and the SARIMA–GRNN combination model was 6.07% and 2.56%, and the determination coefficient was 0.73 and 0.94, respectively. The better performance of the SARIMA–GRNN combination model was further confirmed with the forecasting dataset (2017). TB is a seasonal disease in China, with a predominant peak in spring, and the trend of TB decreased by 3.17% per year. The SARIMA–GRNN model was more effective than the widely used SARIMA model at predicting TB incidence.
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Kim, Taereem, Ju-Young Shin, Hanbeen Kim, Sunghun Kim, and Jun-Haeng Heo. "The Use of Large-Scale Climate Indices in Monthly Reservoir Inflow Forecasting and Its Application on Time Series and Artificial Intelligence Models." Water 11, no. 2 (February 21, 2019): 374. http://dx.doi.org/10.3390/w11020374.
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Climate variability is strongly influencing hydrological processes under complex weather conditions, and it should be considered to forecast reservoir inflow for efficient dam operation strategies. Large-scale climate indices can provide potential information about climate variability, as they usually have a direct or indirect correlation with hydrologic variables. This study aims to use large-scale climate indices in monthly reservoir inflow forecasting for considering climate variability. For this purpose, time series and artificial intelligence models, such as Seasonal AutoRegressive Integrated Moving Average (SARIMA), SARIMA with eXogenous variables (SARIMAX), Artificial Neural Network (ANN), Adaptive Neural-based Fuzzy Inference System (ANFIS), and Random Forest (RF) models were employed with two types of input variables, autoregressive variables (AR-) and a combination of autoregressive and exogenous variables (ARX-). Several statistical methods, including ensemble empirical mode decomposition (EEMD), were used to select the lagged climate indices. Finally, monthly reservoir inflow was forecasted by SARIMA, SARIMAX, AR-ANN, ARX-ANN, AR-ANFIS, ARX-ANFIS, AR-RF, and ARX-RF models. As a result, the use of climate indices in artificial intelligence models showed a potential to improve the model performance, and the ARX-ANN and AR-RF models generally showed the best performance among the employed models.
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Bleidorn, Michel Trarbach, Wanderson De Paula Pinto, Edilson Sarter Braum, Gemael Barbosa Lima, and Claudinei Antonio Montebeller. "MODELAGEM E PREVISÃO DE VAZÕES MÉDIAS MENSAIS DO RIO JUCU, ES, UTILIZANDO O MODELO SARIMA." IRRIGA 24, no. 2 (June 27, 2019): 320–35. http://dx.doi.org/10.15809/irriga.2019v24n2p320-335.
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MODELAGEM E PREVISÃO DE VAZÕES MÉDIAS MENSAIS DO RIO JUCU, ES, UTILIZANDO O MODELO SARIMA
MICHEL TRARBACH BLEIDORN1; WANDERSON DE PAULA PINTO2; EDILSON SARTER BRAUN3; GEMAEL BARBOSA LIMA4 E CLAUDINEI ANTONIO MONTEBELLER5
1Pós-graduando em Certificação Ambiental e Consultoria pela Faculdade de Venda Nova do Imigrante (FAVENI), Av. Ângelo Altoé – nº 888 – Santa Cruz, Venda Nova do Imigrante/ES/Brasil, CEP: 29375-000, michelbleidorn@gmail.com;
2Departamento de Ciências Ambientais, Faculdade da Região Serrana (FARESE), Rua Jequitibá – nº 121- Centro, Santa Maria de Jetibá/ES/Brasil, CEP: 29645-000, wandersondpp@gmail.com;
3Mestrando em Meio Ambiente e Recursos Hídricos do Programa de Pós-Graduação em Ciências Florestais, Universidade Federal do Espírito Santo (UFES), Av. Governador Lidemberg – nº 316 – Centro, Jerônimo Monteiro/ES/Brasil, CEP: 29550-000, edilsonsarter@gmail.com;
4Departamento de Ciências Ambientais, Faculdade da Região Serrana (FARESE), Rua Jequitibá – nº 121- Centro, Santa Maria de Jetibá/ES/Brasil, CEP: 29645-000, gemaelbl@yahoo.com.br;
5Instituto Capixaba de Pesquisa, Assistência Técnica e Extensão Rural (INCAPER), BR 101 – km 151 – Bebedouro, Linhares/ES/Brasil, CEP: 29703-900, cmontebeller@yahoo.com.br.
1 RESUMO
O presente estudo teve por objetivo modelar e realizar estudo de previsão de uma série temporal de vazões médias mensais do rio Jucu, ES. A metodologia aplicada baseou-se na proposta por Box e Jenkins. O modelo a ser considerado é o SARIMA, por incluir a característica de sazonalidade. A identificação da sazonalidade foi realizada através da análise espectral, e sua comprovação estatística pelo teste G de Fisher. A identificação da ordem dos modelos foi feita através da análise gráfica dos correlogramas. Dentre os modelos candidatos, foram selecionados aqueles que obtiveram os menores valores dos critérios de informação. O modelo escolhido foi o SARIMA (1,0,0)(5,1,0)12, que obteve um bom ajuste à série em estudo. O referido modelo foi utilizado para realizar previsões de vazões médias mensais para 12 meses à frente. O modelo ajustado se mostrou adequado para realizar previsões. Os valores previstos estão em divergência dos observados, enfatizando uma crise hídrica sem precedente na série temporal considerada. Os resultados deste estudo podem ser utilizados pelos gestores e utilizadores deste curso hídrico, por apresentar características importantes, tais como períodos de cheias e de escassez. Ressalta-se que esses modelos podem ser melhorados ao considerar variáveis explicativas como precipitação, conhecido como modelos SARIMAX.
Palavras-chave: Recursos Hídricos, Modelagem Hidrológica, SARIMA.
BLEIDORN, M.T.; PINTO, W.P.; BRAUN, E.S.; LIMA, G.B.; MONTEBELLER, C.A.
MODELLING AND PREVISION OF MONTHLY MEAN FLOW OF JUCU RIVER, ES, USING SARIMA MODEL
2 ABSTRACT
This study aimed at modeling and performing a prediction study of a series of monthly mean flows of Jucu River, ES. The applied methodology was based on the proposal by Box and Jenkins. The model to be considered is SARIMA, because it includes the characteristic of seasonality. Seasonality identification was made through spectral analysis, and its statistical verification by Fisher G test. The identification of the order of the models was done through graphic analysis of the correlogram. Among the candidate models, those that obtained the lowest values of the information criteria were selected. The model chosen was SARIMA (1,0,0) (5,1,0)12, which presented a good fit to the series under study. This model was used to forecast monthly average flows for 12 months ahead. The adjusted model was adequate for forecasting. The predicted values differed from those observed, emphasizing an unprecedented water crisis in the time series considered. The results of this study can be used by managers and users of this watercourse, as they present important characteristics such as flood and scarcity periods. It is considered that these models can be improved by considering explanatory variables such as precipitation, known as SARIMAX models.
Keywords: Water resources, Hydrological Modelling, SARIMA.
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PRAHLAD SARKAR, PRADIP BASAK, CHINMAYA SUBHRAJYOTI PANDA, DEB SANKAR GUPTA, MRINMOY RAY, and SABYASACHI MITRA. "Prediction of major pest incidence in Jute crop based on weather variables using statistical and machine learning models: A case study from West Bengal." Journal of Agrometeorology 25, no. 2 (May 25, 2023): 305–11. http://dx.doi.org/10.54386/jam.v25i2.1915.
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Jute crop cultivated in Cooch Behar suffers a substantial amount of physical and economical loss every year due to several major insect pest infestation such as Yellow Mite (Polyphagotarsonemus latus Banks) and Jute Semilooper (Anomis sabulifera Guen). Constructed seasonal plots reveal that for Yellow Mite pest incidence is maximum at 55 DAS, while for Jute Semi Looper it is at 45 DAS. Correlation analysis indicate that the weather parameters such as minimum temperature at current week, maximum RH at one week lag, minimum temperature, minimum and maximum RH at two week lag are significantly correlated with the incidence of Yellow Mite, while in case of Jute Semilooper maximum temperature, minimum and maximum RH at two week lag are significantly correlated. Different forecasting models like ARIMA, ARIMAX, SARIMA, SARIMAX and SVR have been fitted and validated using RMSE values. In case of Jute Semilooper, SARIMAX model is found to be the best fitted model followed by SVR and SARIMA. Similarly, for Yellow Mite ARIMAX model produces the least RMSE value followed by SVR and ARIMA. Following successful model validation, forecasting is done for the year 2022 using the best fitted models.
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Dharmadhikari, Pratiksha Rajendra. "SARIMA – A Model for Forecasting Product order demand." International Journal for Research in Applied Science and Engineering Technology 9, no. 10 (October 31, 2021): 1284–89. http://dx.doi.org/10.22214/ijraset.2021.38575.
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Abstract: Product analysis is the most important part for any working manufacturing. It provides the sales record of their currently manufactured product and also it helps to predict its performance in the future. For this analysis, a SARIMAX model has been used with Time series forecasting. This paper will explain the need of such model instead of using a simple regression model to predict the order demand. This study analyses and presents a forecasting model to predict an order demand for the Product over the time period. Demand in Product is a main component for planning all processes in supply chain, and therefore determining Product demand is a great interest for supply chain. Mean forecasting for product order demand was carried out using SARIMA model, by using the past data from the period of 2011 to 2017. The model with the least value of Akaike Information Criterion (AIC) was selected as the appropriate model for forecasting mean Error. Test for normality of residuals were performed to see the adequacy of the chosen model. SARIMA (1, 1, 1) (0, 1, 1) (12) was selected as the best model for mean product order demand forecast. The results obtained will prove that the model could be utilized to forecast the future demand in the Product manufacturing industry. These results will help the manufacturers for manufacturing reliable guidelines in making decisions. Keywords: ARIMA, AIC, S-ARIMA, Regression
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Adineh, Amir Hossein, Zahra Narimani, and Suresh Chandra Satapathy. "Importance of data preprocessing in time series prediction using SARIMA: A case study." International Journal of Knowledge-based and Intelligent Engineering Systems 24, no. 4 (January 18, 2021): 331–42. http://dx.doi.org/10.3233/kes-200065.
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Over last decades, time series data analysis has been in practice of specific importance. Different domains such as financial data analysis, analyzing biological data and speech recognition inherently deal with time dependent signals. Monitoring the past behavior of signals is a key for precise predicting the behavior of a system in near future. In scenarios such as financial data prediction, the predominant signal has a periodic behavior (starting from beginning of the month, week, etc.) and a general trend and seasonal behavior can also be assumed. Autoregressive Integrated Moving Average (ARIMA) model and its seasonal extension, SARIMA, have been widely used in forecasting time-series data, and are also capable of dealing with the seasonal behavior/trend in the data. Although the behavior of data may be autoregressive and trends and seasonality can be detected and handled by SARIMA, the data is not always exactly compatible with SARIMA (or more generally ARIMA) assumptions. In addition, the existence of missing data is not pre-assumed in SARIMA, while in real-world, there can be always missing data for different reasons such as holidays for which no data may be recorded. For different week days, different working hours may be a cause of observing irregular patterns compared to what is expected by SARIMA assumptions. In this paper, we investigate the effectiveness of applying SARIMA on such real-world data, and demonstrate preprocessing methods that can be applied in order to make the data more suitable to be modeled by SARIMA model. The data in the existing research is derived from transactions of a mutual fund investment company, which contains missing values (single point and intervals) and also irregularities as a result of the number of working hours per week days being different from each other which makes the data inconsistent leading to poor result without preprocessing. In addition, the number of data points was not adequate at the time of analysis in order to fit a SARIM model. Preprocessing steps such as filling missing values and tricks to make data consistent has been proposed to deal with existing problems. Results show that prediction performance of SARIMA on this set of real-world data is significantly improved by applying several preprocessing steps introduced in order to deal with mentioned circumstances. The proposed preprocessing steps can be used in other real-world time-series data analysis.
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Monica, M., A. Suharsono, B. W. Otok, and A. Wibisono. "Hybrid SARIMA-FFNN model in forecasting cash outflow and inflow." Journal of Physics: Conference Series 2106, no. 1 (November 1, 2021): 012002. http://dx.doi.org/10.1088/1742-6596/2106/1/012002.
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Abstract The monthly inflow and outflow of money from an area is one of the important concerns in the economic life of a region. This study aims to model and predict the monthly cash inflow and outflow of Kediri, East Java Province, Indonesia using the Hybrid Seasonal Autoregressive Integrated Moving Average – Feedforward Neural Network (SARIMA-FFNN) model. Seasonal time series data from monthly cash inflow and outflow of Kediri are used to test the forecasting accuracy of the proposed hybrid model. First, both variables are modeled using the SARIMA model. Then, non-linearity testing was carried out on the best SARIMA model for each variable and the results showed that only cash inflow was non-linear. Therefore, only cash inflow could be continued with the FFNN model. The best selected model was the FFNN model with the input SARIMA(0,0,0)(1,0,0)12 with five hidden layers. The input of FFNN modeling was based on the best SARIMA model with only the autoregressive order which for non-seasonal and seasonal. The sum of hidden layers was chosen by the smallest values of MAPE and RMSE. Forecasting results with the hybrid SARIMA-FFNN model on data testing followed the actual data pattern.
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Joseph, Agnes B., and Godfrey Edward Mpogolo. "Application of SARIMA Model on Forecasting Wholesale Prices of Food Commodities in Tanzania: A Case of Maize, Rice and Beans." African Journal of Accounting and Social Science Studies 4, no. 1 (August 18, 2022): 206–19. http://dx.doi.org/10.4314/ajasss.v4i1.11.
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This research used a time series model called the Seasonal Autoregressive Integrated Moving Average (SARIMA) technique to model and forecast wholesale prices of Tanzania`s key food crops, notably maize, rice, and beans. The SARIMA model was selected due to its ability of fitting data with seasonality. Monthly wholesale prices data of the three crops between February 2004 to October 2021 in Tanzania were retrieved from the website of the Bank of Tanzania (BoT), resulting in 213 observations on each crop. The data from February 2004 to October, 2020 were used to fit a SARIMA model and data of November 2020 to October 2021 were used to validate the model. The results show that SARIMA (0,1,2) (1,0,1)12, SARIMA (0,1,0) (1,1,1)12 and SARIMA (0,1,0) (0,1,1)12 are the most suitable models for forecasting wholesale prices of maize, rice and beans respectively. The model’s accuracy was tested using Mean Absolute Percent Error (MAPE), and the results were found satisfactory. The results reveal that maize, rice, and beans will all have higher peak prices in February 2022, with TZS 54,083/=, TZS 167,258/=, and TZS 180,117.68/= per 100kg, respectively. Therefore, SARIMA (0,1,2)(1,0,1)12, SARIMA (0,1,0)(1,1,1)12 and SARIMA (0,1,0) (0,1,1)12 models could serve as a useful tool for modelling and forecasting monthly wholesale prices of maize, rice and beans respectively in Tanzania.
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Ningsih, Prawati, Maiyastri Maiyastri, and Yudiantri Asdi. "PERAMALAN JUMLAH KEDATANGAN WISATAWAN MANCANEGARA KE SUMATERA BARAT MELALUI BANDARA INTERNASIONAL MINANGKABAU DENGAN MODEL SARIMA." Jurnal Matematika UNAND 8, no. 2 (July 15, 2019): 128. http://dx.doi.org/10.25077/jmu.8.2.128-134.2019.
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Jumlah kedatangan wisatawan mancanegara ke Sumatera Barat melalui Bandara Internasional Minangkabau cenderung mengalami perubahan di setiap tahunnya. Untuk mengetahui jumlah kedatangan wisatawan mancanegara di masa yang akan datang, dapat dilakukan dengan menggunakan model SARIMA. Model SARIMA merupakan model ARIMA yang mengandung unsur musiman. Model ini diaplikasikan untuk meramalkan jumlah kedatangan wisatawan mancanegara pada periode Januari 2019 hingga Desember 2019. Hasil analisis data menunjukkan bahwa model SARIMA(1, 0, 1)(2, 1, 0)12 yang terbaik, dimana hasil pendugaan yang diperoleh tidak jauh berbeda dari data aktual.Kata Kunci: Wisatawan Mancanegara, Model SARIMA, Peramalan
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Wibisono, Dwi Anugrah, Dian Anggraeni, and Alfian Futuhul Hadi. "PERBAIKAN MODEL SEASONAL ARIMA DENGAN METODE ENSEMBLE KALMAN FILTER PADA HASIL PREDIKSI CURAH HUJAN." Majalah Ilmiah Matematika dan Statistika 19, no. 1 (March 12, 2019): 9. http://dx.doi.org/10.19184/mims.v19i1.17262.
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Forecasting is a time series analytic that used to find out upcoming improvement in the next event using past events as a reference. One of the forecasting models that can be used to predict a time series is Kalman Filter method. The modification of the estimation method of Kalman Filter is Ensemble Kalman Filter (EnKF). This research aims to find the result of EnKF algorithm implementation on SARIMA model. To start with, preticipation forecast data is changed in the form of SARIMA model to obtain some SARIMA model candidates. Next, this best model of SARIMA applied to Kalman Filter models. After Kalman Filter models created, forecasting could be done by applying pass rainfall data to the models. It can be used to predict rainfall intensity for next year. The quality of this forecasting can be assessed by looking at MAPE’s value and RMSE’s value. This research shows that enkf method relative can fix sarima method’s model, proved by mape and rmse values which are smaller and indicate a more accurate prediction.
Keywords: Ensemble Kalman Filter, Forecast, SARIMA
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Luo, Chang Shou, Li Ying Zhou, and Qing Feng Wei. "Application of SARIMA Model in Cucumber Price Forecast." Applied Mechanics and Materials 373-375 (August 2013): 1686–90. http://dx.doi.org/10.4028/www.scientific.net/amm.373-375.1686.
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The price of vegetables is difficult to predict. In order to find an effective method, this paper fully considers the seasonal variations, and uses the seasonal auto regressive integrated moving average model （SARIMA） to forecast the cucumber price. The experimental results indicate that the SARIMA(1,0,1)(1,1,1)12 fits the cucumber market prices exactly in the previous months. Its average fitting error is 17%. The forecast data of twelve months in 2011 is in line with the actual trend. Its average error reaches 25%. The SARIMA model is feasible for short-term warning of vegetable price.
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Tahyudin, Imam, Rizki Wahyudi, Wiga Maulana, and Hidetaka Nambo. "The mortality modeling of covid-19 patients using a combined time series model and evolutionary algorithm." International Journal of Advances in Intelligent Informatics 8, no. 1 (March 31, 2022): 69. http://dx.doi.org/10.26555/ijain.v8i1.669.
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COVID-19 pandemics for as long as two years ago since 2019 gives many insights into various aspects, including scientific development. One of them is the fundamental research of computer science. This research aimed to construct the best model of COVID-19 patients’ mortality and obtain less prediction errors. We performed the combination methods of time series, SARIMA, and Evolutionary algorithm, PARCD, to predict male patients who died because of COVID-19 in the USA, containing 1.008 data. So, this research proposed that SARIMA-PARCD has a powerful combination for addressing the complex problem in a dataset. The prediction error of SARIMA-PARCD was compared with other methods, i.e., SARIMA, LSTM, and the combination of SARIMA-LSTM. The result showed that the SARIMA-PARCD has the smallest MSE value of 0.0049. Therefore, the proposed method is competitive to implement in other cases with similar characteristics. This combination is robust for solving linear and non-linear problems.
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FM, Mohammed Farooq Abdulla, Tamilselvan V, Harshini V S, and Deepthikka R S. "Purchase and Analytics for Grace Marketing." International Journal of Engineering Research in Computer Science and Engineering 9, no. 5 (May 14, 2022): 21–24. http://dx.doi.org/10.36647/ijercse/09.05.art003.
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In recent years development of computer systems were able to learn and adapt without following explicit instructions, by using algorithms and statistical models to analyze and draw inferences from patterns in data is known as machine learning.In this phase sales of different lubricants were predicted using a multivariate time series forecasting algorithm.Previously it showed that the model was accurate in predicting the engine oil sales for a particular time.Using Regressions the accuracy of sales prediction was less (74%) and the models like SVM and Random forest were showing signs of over fitting.The accuracy obtained in the multivariate time series forecasting was good than other algorithms.Time series algorithms are used extensively for forecasting time-based data.In time series ARIMA,SARIMA and SARIMAX are the common methods to forecast time based data.SARIMAX are efficient in forecasting data which has seasonality trends than ARIMA which are good in forecasting data which is stationary in nature Time series methods are extensively used for forecasting time based data.In time series ARIMA,SARIMA and SARIMAX are the common methods to forecast tie based data.ARIMA is the abbreviation of Auto Regressive Integrated Moving Average a model which explains a given time series model based on its lags and other values.SARIMAX is the abbreviation of Seasonal Auto Regressive Integrated Moving Average with Xegeneous variables. ARIMA model is best for forecasting stationary time series data and SARIMAX is used for forecasting values which is seasonal in nature.
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FM, Mohammed Farooq Abdulla, Tamilselvan V, Harshini V S, and Deepthikka R S. "Purchase and Analytics for Grace Marketing." International Journal of Science, Engineering and Management 9, no. 4 (April 25, 2022): 1–4. http://dx.doi.org/10.36647/ijsem/09.04.a001.
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In recent years development of computer systems were able to learn and adapt without following explicit instructions, by using algorithms and statistical models to analyze and draw inferences from patterns in data is known as machine learning.In this phase sales of different lubricants were predicted using a multivariate time series forecasting algorithm.Previously it showed that the model was accurate in predicting the engine oil sales for a particular time.Using Regressions the accuracy of sales prediction was less (74%) and the models like SVM and Random forest were showing signs of over fitting.The accuracy obtained in the multivariate time series forecasting was good than other algorithms.Time series algorithms are used extensively for forecasting time-based data.In time series ARIMA,SARIMA and SARIMAX are the common methods to forecast time based data.SARIMAX are efficient in forecasting data which has seasonality trends than ARIMA which are good in forecasting data which is stationary in nature Time series methods are extensively used for forecasting time based data.In time series ARIMA,SARIMA and SARIMAX are the common methods to forecast tie based data.ARIMA is the abbreviation of Auto Regressive Integrated Moving Average a model which explains a given time series model based on its lags and other values.SARIMAX is the abbreviation of Seasonal Auto Regressive Integrated Moving Average with Xegeneous variables. ARIMA model is best for forecasting stationary time series data and SARIMAX is used for forecasting values which is seasonal in nature.
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Chikobvu, Delson, and Caston Sigauke. "Regression-SARIMA modelling of daily peak electricity demand in South Africa." Journal of Energy in Southern Africa 23, no. 3 (August 1, 2012): 23–30. http://dx.doi.org/10.17159/2413-3051/2012/v23i3a3169.
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In this paper, seasonal autoregressive integrated moving average (SARIMA) and regression with SARIMA errors (regression-SARIMA) models are developed to predict daily peak electricity demand in South Africa using data for the period 1996 to 2009. The performance of the developed models is evaluated by comparing them with Winter’s triple exponential smoothing model. Empirical results from the study show that the SARIMA model produces more accurate short-term forecasts. The regression-SARIMA modelling framework captures important drivers of electricity demand. These results are important to decision makers, load forecasters and systems operators in load flow analysis and scheduling of electricity.
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Shafaei, Maryam, Jan Adamowski, Ahmad Fakheri-Fard, Yagob Dinpashoh, and Kazimierz Adamowski. "A wavelet-SARIMA-ANN hybrid model for precipitation forecasting." Journal of Water and Land Development 28, no. 1 (March 1, 2016): 27–36. http://dx.doi.org/10.1515/jwld-2016-0003.
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Abstract Given its importance in water resources management, particularly in terms of minimizing flood or drought hazards, precipitation forecasting has seen a wide variety of approaches tested. As monthly precipitation time series have nonlinear features and multiple time scales, wavelet, seasonal auto regressive integrated moving average (SARIMA) and hybrid artificial neural network (ANN) methods were tested for their ability to accurately predict monthly precipitation. A 40-year (1970–2009) precipitation time series from Iran’s Nahavand meteorological station (34°12’N lat., 48°22’E long.) was decomposed into one low frequency subseries and several high frequency sub-series by wavelet transform. The low frequency sub-series were predicted with a SARIMA model, while high frequency subseries were predicted with an ANN. Finally, the predicted subseries were reconstructed to predict the precipitation of future single months. Comparing model-generated values with observed data, the wavelet-SARIMA-ANN model was seen to outperform wavelet-ANN and wavelet-SARIMA models in terms of precipitation forecasting accuracy.
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Ma, Shuqi, Qianyi Liu, and Yudong Zhang. "A prediction method of fire frequency: Based on the optimization of SARIMA model." PLOS ONE 16, no. 8 (August 9, 2021): e0255857. http://dx.doi.org/10.1371/journal.pone.0255857.
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In the current study, based on the national fire statistics from 2003 to 2017, we analyzed the 24-hour occurrence regularity of fire in China to study the occurrence regularity and influencing factors of fire and provide a reference for scientific and effective fire prevention. The results show that the frequency of fire is low from 0 to 6 at night, accounting for about 13.48%, but the death toll due to fire is relatively high, accounting for about 39.90%. Considering the strong seasonal characteristics of the time series of monthly fire frequency, the SARIMA model predicts the fire frequency. According to the characteristics of time series data and prediction results, an optimized Seasonal Autoregressive Integrated Moving Average Model (SARIMA) model based on Quantile outlier detection method and similar mean interpolation method is proposed, and finally, the optimal model is constructed as SARIMA (1,1,1) (1,1,1) 12 for prediction. The results show that: according to the optimized SARIMA model to predict the number of fires in 2018 and 2019, the root mean square error of the fitting results is 2826.93, which is less than that of the SARIMA model, indicating that the improved SARIMA model has a better fitting effect. The accuracy of the results is increased by 11.5%. These findings verified that the optimized SARIMA model is an effective improvement for the series with quantile outliers, and it is more suitable for the data prediction with seasonal characteristics. The research results can better mine the law of fire aggregation and provide theoretical support for fire prevention and control work of the fire department.
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Makatjane, Katleho Daniel, Edward Kagiso Molefe, and Roscoe Bertrum Van Wyk. "The Analysis of the 2008 US Financial Crisis: An Intervention Approach." Journal of Economics and Behavioral Studies 10, no. 1(J) (March 15, 2018): 59–68. http://dx.doi.org/10.22610/jebs.v10i1(j).2089.
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The current study investigates the impact of the 2008 US financial crises on the real exchange rate in South Africa. The data used in this empirical analysis is for the period from January 2000 to June 2017. The Seasonal autoregressive integrated moving average (SARIMA) intervention charter was used to carry out the analysis. Results revealed that the financial crises period in South Africa occurred in March 2008 and significantly affected the exchange rate. Hence, the impact pattern was abrupt. Using the SARIMA model as a benchmark, four error metrics; to be precise mean absolute error (MAE), mean absolute percentage error (MAPE), mean error (ME) and Mean percentage error (MPE) was used to assess the performance of the intervention model and SARIMA model. The results of the SARIMA intervention model produced better forecasts as compared to that one of SARIMA model.
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Makatjane, Katleho Daniel, Edward Kagiso Molefe, and Roscoe Bertrum Van Wyk. "The Analysis of the 2008 US Financial Crisis: An Intervention Approach." Journal of Economics and Behavioral Studies 10, no. 1 (March 15, 2018): 59. http://dx.doi.org/10.22610/jebs.v10i1.2089.
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The current study investigates the impact of the 2008 US financial crises on the real exchange rate in South Africa. The data used in this empirical analysis is for the period from January 2000 to June 2017. The Seasonal autoregressive integrated moving average (SARIMA) intervention charter was used to carry out the analysis. Results revealed that the financial crises period in South Africa occurred in March 2008 and significantly affected the exchange rate. Hence, the impact pattern was abrupt. Using the SARIMA model as a benchmark, four error metrics; to be precise mean absolute error (MAE), mean absolute percentage error (MAPE), mean error (ME) and Mean percentage error (MPE) was used to assess the performance of the intervention model and SARIMA model. The results of the SARIMA intervention model produced better forecasts as compared to that one of SARIMA model.
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Wang, You, Ruxue Jia, Fang Dai, and Yunxia Ye. "Traffic Flow Prediction Method Based on Seasonal Characteristics and SARIMA-NAR Model." Applied Sciences 12, no. 4 (February 19, 2022): 2190. http://dx.doi.org/10.3390/app12042190.
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Traffic flow is used as an essential indicator to measure the performance of the road network and a pivotal basis for road classification. However, the combined prediction model of traffic flow based on seasonal characteristics has been given little attention at present. Because the seasonal autoregressive integrated moving average model (SARIMA) has superior linear fitting characteristics, it is often used to process seasonal time series. In contrast, the non-autoregressive dynamic neural network (NAR) has a vital memory function and nonlinear interpretation capabilities. They are suitable for constructing combined forecasting models. The traffic flow time series of a highway in southwest China is taken as the research object in this paper. Combining the SARIMA (0,1,2) (0,1,2)12 model and the NAR model with 15 hidden layer neurons and fourth-order delay, two combined models are constructed: the linear and nonlinear component combination method is realized by the SARIMA-NAR combination model 1, and the MSE weight combination method is used by the SARIMA-NAR combination model 2. We calculated that the prediction accuracy of SARIMA-NAR combined model 1 is as high as 0.92, and the prediction accuracy of SARIMA-NAR combined model 2 is 0.90. In addition, the traffic flow forecast under the influence of the epidemic is also discussed. Through a comprehensive comparison of multiple indicators, the results show that the SARIMA-NAR combined model 1 has better road traffic flow fitting and prediction effects and is suitable for the greater volatility of traffic flow during the epidemic. This model improves the effectiveness and reliability of traffic flow forecasting, and the forecasting process is more convenient and efficient.
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Wang, Yongbin, Chunjie Xu, Shengkui Zhang, Zhende Wang, Li Yang, Ying Zhu, and Juxiang Yuan. "Temporal trends analysis of tuberculosis morbidity in mainland China from 1997 to 2025 using a new SARIMA-NARNNX hybrid model." BMJ Open 9, no. 7 (July 2019): e024409. http://dx.doi.org/10.1136/bmjopen-2018-024409.
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ObjectiveTuberculosis (TB) remains a major deadly threat in mainland China. Early warning and advanced response systems play a central role in addressing such a wide-ranging threat. The purpose of this study is to establish a new hybrid model combining a seasonal autoregressive integrated moving average (SARIMA) model and a non-linear autoregressive neural network with exogenous input (NARNNX) model to understand the future epidemiological patterns of TB morbidity.MethodsWe develop a SARIMA-NARNNX hybrid model for forecasting future levels of TB incidence based on data containing 255 observations from January 1997 to March 2018 in mainland China, and the ultimate simulating and forecasting performances were compared with the basic SARIMA, non-linear autoregressive neural network (NARNN) and error-trend-seasonal (ETS) approaches, as well as the SARIMA-generalised regression neural network (GRNN) and SARIMA-NARNN hybrid techniques.ResultsIn terms of the root mean square error, mean absolute error, mean error rate and mean absolute percentage error, the identified best-fitting SARIMA-NARNNX combined model with 17 hidden neurons and 4 feedback delays had smaller values in both in-sample simulating scheme and the out-of-sample forecasting scheme than the preferred single SARIMA(2,1,3)(0,1,1)12model, a NARNN with 19 hidden neurons and 6 feedback delays and ETS(M,A,A), and the best-performing SARIMA-GRNN and SARIMA-NARNN models with 32 hidden neurons and 6 feedback delays. Every year, there was an obvious high-risk season for the notified TB cases in March and April. Importantly, the epidemic levels of TB from 2006 to 2017 trended slightly downward. According to the projection results from 2018 to 2025, TB incidence will continue to drop by 3.002% annually but will remain high.ConclusionsThe new SARIMA-NARNNX combined model visibly outperforms the other methods. This hybrid model should be used for forecasting the long-term epidemic patterns of TB, and it may serve as a beneficial and effective tool for controlling this disease.
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Azad, Abdus Samad, Rajalingam Sokkalingam, Hanita Daud, Sajal Kumar Adhikary, Hifsa Khurshid, Siti Nur Athirah Mazlan, and Muhammad Babar Ali Rabbani. "Water Level Prediction through Hybrid SARIMA and ANN Models Based on Time Series Analysis: Red Hills Reservoir Case Study." Sustainability 14, no. 3 (February 5, 2022): 1843. http://dx.doi.org/10.3390/su14031843.
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Reservoir water level (RWL) prediction has become a challenging task due to spatio-temporal changes in climatic conditions and complicated physical process. The Red Hills Reservoir (RHR) is an important source of drinking and irrigation water supply in Thiruvallur district, Tamil Nadu, India, also expected to be converted into the other productive services in the future. However, climate change in the region is expected to have consequences over the RHR’s future prospects. As a result, accurate and reliable prediction of the RWL is crucial to develop an appropriate water release mechanism of RHR to satisfy the population’s water demand. In the current study, time series modelling technique was adopted for the RWL prediction in RHR using Box–Jenkins autoregressive seasonal autoregressive integrated moving average (SARIMA) and artificial neural network (ANN) hybrid models. In this research, the SARIMA model was obtained as SARIMA (0, 0, 1) (0, 3, 2)12 but the residual of the SARIMA model could not meet the autocorrelation requirement of the modelling approach. In order to overcome this weakness of the SARIMA model, a new SARIMA–ANN hybrid time series model was developed and demonstrated in this study. The average monthly RWL data from January 2004 to November 2020 was used for developing and testing the models. Several model assessment criteria were used to evaluate the performance of each model. The findings showed that the SARIMA–ANN hybrid model outperformed the remaining models considering all performance criteria for reservoir RWL prediction. Thus, this study conclusively proves that the SARIMA–ANN hybrid model could be a viable option for the accurate prediction of reservoir water level.
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Arunraj, Nari Sivanandam, Diane Ahrens, and Michael Fernandes. "Application of SARIMAX Model to Forecast Daily Sales in Food Retail Industry." International Journal of Operations Research and Information Systems 7, no. 2 (April 2016): 1–21. http://dx.doi.org/10.4018/ijoris.2016040101.
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During retail stage of food supply chain (FSC), food waste and stock-outs occur mainly due to inaccurate sales forecasting which leads to inappropriate ordering of products. The daily demand for a fresh food product is affected by external factors, such as seasonality, price reductions and holidays. In order to overcome this complexity and inaccuracy, the sales forecasting should try to consider all the possible demand influencing factors. The objective of this study is to develop a Seasonal Autoregressive Integrated Moving Average with external variables (SARIMAX) model which tries to account all the effects due to the demand influencing factors, to forecast the daily sales of perishable foods in a retail store. With respect to performance measures, it is found that the proposed SARIMAX model improves the traditional Seasonal Autoregressive Integrated Moving Average (SARIMA) model.
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Chen, Bin, and Jin Wu. "Predicting Model for Air Transport Demand under Uncertainties Based on Particle Filter." Sustainability 14, no. 24 (December 13, 2022): 16694. http://dx.doi.org/10.3390/su142416694.
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The outbreak of the COVID-19 has brought about huge economic loss and civil aviation industries all over the world have suffered severe damage. An effective method is urgently needed to accurately predict air-transport demand under the influences of such accidental factors. This paper proposes a novel predicting framework for the air-transport demand considering the uncertainties caused by accidental factors including regional wars, climatic anomalies, and virus outbreaks. By employing a seasonal autoregressive integrated moving average (sARIMA) model as the basic model, a particle filter (PF)-based sARIMA-pf model is proposed. The applicability of adapting the high-order sARIMA model as the state transition model in a PF framework is shown and proven to be effective. The proposed method has the advantage of coping with short-term prediction with known uncertainties. By conducting case studies on the prediction of air passenger traffic volume in China, the sARIMA-pf model showed better performance than the sARIMA model and improved the accuracy by 49.29% and 44.96% under the conventional and pandemic scenarios, respectively, when using the root mean square error (RMSE) as the indicator.
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Akermi, Seif Eddine, Mohamed L’Hadj, and Schehrazad Selmane. "Epidemiology and Time Series Analysis of Human Brucellosis in Tebessa Province, Algeria, from 2000 to 2020." Journal of Research in Health Sciences 22, no. 1 (October 31, 2021): e00544-e00544. http://dx.doi.org/10.34172/jrhs.2022.79.
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Background: Brucellosis runs rampant endemically with sporadic outbreaks in Algeria. The present study aimed to provide insights into the epidemiology of brucellosis and compare the performance of some prediction models using surveillance data from Tebessa province, Algeria. Study Design: A retrospective study. Methods: Seasonal autoregressive integrated moving average (SARIMA), neural network autoregressive (NNAR), and hybrid SARIMA-NNAR models were developed to predict monthly brucellosis notifications. The prediction performance of these models was compared using root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). Results: Overall, 13670 human brucellosis cases were notified in Tebessa province from 2000-2020 with a male-to-female ratio of 1.3. The most affected age group was 15-44 years (56.2%). The cases were reported throughout the year with manifest seasonality. The annual notification rate ranged from 30.9 (2013) to 246.7 (2005) per 100000 inhabitants. The disease was not evenly distributed, rather spatial and temporal variability was observed. The SARIMA (2,1,3) (1,1,1)12, NNAR (12,1,6)12, and SARIMA (2,0,2) (1,1,0)12- NNAR (5,1,4)12 were selected as the best-fitting models. The RMSE, MAE, and MAPE of the SARIMA and SARIMA-NNAR models were by far lower than those of the NNAR model. Moreover, the SARIMA-NNNAR hybrid model achieved a slightly better prediction accuracy for 2020 than the SARIMA model. Conclusion: As evidenced by the obtained results, both SARIMA and hybrid SARIMA-NNAR models are suitable to predict human brucellosis cases with high accuracy. Reasonable predictions, along with mapping brucellosis incidence, could be of great help to veterinary and health policymakers in the development of informed, effective, and targeted policies, as well as timely interventions.
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AISHAH, NURUL, DODI DEVIANTO, and MAIYASTRI MAIYASTRI. "PEMODELAN JUMLAH KUNJUNGAN WISATAWAN MANCANEGARA KE INDONESIA MELAUI BANDARA NGURAH RAI BALI DENGAN MODEL SARIMA-ARCH." Jurnal Matematika UNAND 10, no. 3 (July 26, 2021): 248. http://dx.doi.org/10.25077/jmu.10.3.248-259.2021.
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Suatu data deret waktu dipengaruhi oleh beberapa faktor, seperti faktor trend dan faktor musiman. Data deret waktu yang dipengaruhi oleh trend dan musiman dapat dimodelkan dengan model SARIMA. Namun, model SARIMA tidak selalu menghasilkan ragam sisaan yang konstan pada data musiman yang berfluktuasi tinggi atau model yang diperoleh dipengaruhi oleh efek heteroskedastisitas. Salah satu model yang dapat mengatasi efek heteroskedastisitas adalah model ARCH/GARCH. Oleh karena itu, digunakan model ARCH/GARCH untuk mengatasi heteroskedastisitas pada data musiman . Penelitian dilakukan menggunakan data musiman yang juga memiliki fluktuasi tinggi, yaitu data jumlah kunjungan wisatawan mancanegara ke Indonesia melalui bandara Ngurah Rai Bali pada bulan Januari 2008 sampai Desember 2019. Model terbaik yang diperoleh untuk data tersebut adalah SARIMA(0,1,1)(0, 1, 1)12 ARCH(1).Kata Kunci: SARIMA, ARCH, GARCH
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Tadesse, Kassahun Birhanu, and Megersa Olumana Dinka. "Application of SARIMA model to forecasting monthly flows in Waterval River, South Africa." Journal of Water and Land Development 35, no. 1 (December 1, 2017): 229–36. http://dx.doi.org/10.1515/jwld-2017-0088.
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AbstractKnowledge of future river flow information is fundamental for development and management of a river system. In this study, Waterval River flow was forecasted by SARIMA model using GRETL statistical software. Mean monthly flows from 1960 to 2016 were used for modelling and forecasting. Different unit root and Mann–Kendall trend analysis proved the stationarity of the observed flow time series. Based on seasonally differenced correlogram characteristics, different SARIMA models were evaluated; their parameters were optimized, and diagnostic check up of forecasts was made using white noise and heteroscedasticity tests. Finally, based on minimum Akaike Information (AI) and Hannan–Quinn (HQ) criteria, SARIMA (3, 0, 2) x (3, 1, 3)12 model was selected for Waterval River flow forecasting. Comparison of forecast performance of SARIMA models with that of computational intelligent forecasting techniques was recommended for future study.
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Onyeka-Ubaka, J. N., M. A. Halid, and R. K. Ogundeji. "Optimal Stochastic Forecast Models of Rainfall in South-West Region of Nigeria." International Journal of Mathematical Analysis and Optimization: Theory and Applications 7, no. 2 (November 16, 2021): 1–20. http://dx.doi.org/10.52968/28306097.
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Rainfall estimates are important components of water resources applications, especially in agriculture, transport constructing irrigation and drainage systems. This paper aims to stochastically model and forecast the rainfall trend and pattern for a city, each purposively selected in five states of the South-Western Region of Nigeria. The data collected from Nigerian Meteorological Agency (NIMET) website are captured with fractional autoregressive integrated moving average (ARFIMA) and seasonal autoregressive integrated moving average (SARIMA) models. The autocorrelation function (ACF) and partial autocorrelation function (PACF) are used for model identification, the models selected are subjected to diagnostic checks for the models adequacy. Several tests: Augmented Dickey Fuller (ADF), Ljung Box and Jarque Bera tests are used for investigating unit root, serial autocorrelation and normality of residuals, respectively; the mean square error, root mean square error and mean absolute error are employed in validating the optimal stochastic model for each city in all states, in which the model with the lowest error of forecasting of all competing models is suggested as the best. The analyses and findings suggest SARIMA(1,0,1)(1,1,0) [12], SARIMA(3,0,2)(1,0,0) [12], SARIMA(1,0,0)(1,1,0) [12], SARIMA(2,0,2)(2,1,0) [12] and SARIMA(0,0,1)(1,1,0) [12] for (Ibadan) Oyo State, (Ikorodu) Lagos State, (Osogbo) Osun State, (Abeokuta) Ogun State and (Akure) Ondo state, respectively. The seasonal ARIMA (SARIMA) model was proven to be the best optimal stochastic forecast model for forecasting rainfall in the selected cities. The SARIMA model was, therefore, recommended as a veritable technique that will assist decision makers (Government, Farmers, and Policymakers) to establish better strategies “aprior” on the management of rainfall against upcoming weather changes to ensure increase in agricultural yields for the betterment of the citizenry and general economic growth.
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Ghide, Luwam, Siyuan Wei, and Yiming Ding. "Comparative Study of Wavelet-SARIMA and EMD-SARIMA for Forecasting Daily Temperature Series." International Journal of Analysis and Applications 20 (March 18, 2022): 17. http://dx.doi.org/10.28924/2291-8639-20-2022-17.
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This paper aims to find a forecasting model based on the comparative study of wavelet- ARIMA and EMD-ARIMA models and residual-based model selection technique for temperature time series. Time series analysis is essential in studying temperature data for investigating the variation and predicting the future trend, in which we can control the changes and make good decisions. And most important is to understand the trend in the series with time. This study applied hybridized models of wavelet transform and empirical mode decomposition with seasonal autoregressive integrated moving average (SARIMA), which combines two models to get better accuracy, for forecasting daily average temperature time series data in the central region of Eritrea, Asmara. Daily data was collected for 30 years, from January 1, 1991, to December 31, 2020. The study compares WT-SARIMA and EMD-SARIMA models to find a well fit and better forecasting model. Model selection techniques are essential for time series analysis to determine which model best fits our data. AIC and BIC are the most used methods in model selection. This paper uses an additional method based on the residual series. In estimating accurate parameters, the structure of the residual sequence had a lot of connection, in which a stationary residual depict an accurate estimation. From this perspective, a nonstationarity measurement of the residual series is used for model selection. The relative performance is based on the predictive capability of sample forecasts assessed. The results indicate that the hybridized wavelet-SARIMA model is more effective than the other models, and MATLAB soft-wire is used for this analysis.
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Anisimov, E. S., and J. M. Beketnova. "Development of a model for predicting money laundering rate." Vestnik Universiteta, no. 5 (July 1, 2022): 136–43. http://dx.doi.org/10.26425/1816-4277-2022-5-136-143.
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The article suggests model for predicting the level of money laundering on the basis of data from the Ministry of Internal Affairs of the Russian Federation on the state of economic crime in Russia since the beginning of 2011. Using a seasonally integrated autoregressive moving average (SARIMA) model, it compares different regression models for the research tasks (linear regression, logistic regression, autoregressive and SARIMA). The necessity of taking into account seasonal regularities in the structure of money laundering was underlined, and the SARIMA model with the lowest deviations from the actual values was chosen. The necessity of taking into account seasonal regularities in the structure of money laundering was underlined, and the SARIMA model with the lowest deviations from the actual values was chosen. The article presents the results of data analysis using the method of least squares, calculating the mean squared error (MSE). High accuracy of short-term forecasts was noted: the deviation from the actual number of cases is about three cases (with the average number of cases being 68 over the last 10 years). The forecasting model can be recommended for implementation in the analytical complexes of financial monitoring and supervisory authorities.
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Yu, Gongchao, Huifen Feng, Shuang Feng, Jing Zhao, and Jing Xu. "Forecasting hand-foot-and-mouth disease cases using wavelet-based SARIMA–NNAR hybrid model." PLOS ONE 16, no. 2 (February 5, 2021): e0246673. http://dx.doi.org/10.1371/journal.pone.0246673.
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Background Hand-foot-and-mouth disease_(HFMD) is one of the most typical diseases in children that is associated with high morbidity. Reliable forecasting is crucial for prevention and control. Recently, hybrid models have become popular, and wavelet analysis has been widely performed. Better prediction accuracy may be achieved using wavelet-based hybrid models. Thus, our aim is to forecast number of HFMD cases with wavelet-based hybrid models. Materials and methods We fitted a wavelet-based seasonal autoregressive integrated moving average (SARIMA)–neural network nonlinear autoregressive (NNAR) hybrid model with HFMD weekly cases from 2009 to 2016 in Zhengzhou, China. Additionally, a single SARIMA model, simplex NNAR model, and pure SARIMA–NNAR hybrid model were established for comparison and estimation. Results The wavelet-based SARIMA–NNAR hybrid model demonstrates excellent performance whether in fitting or forecasting compared with other models. Its fitted and forecasting time series are similar to the actual observed time series. Conclusions The wavelet-based SARIMA–NNAR hybrid model fitted in this study is suitable for forecasting the number of HFMD cases. Hence, it will facilitate the prevention and control of HFMD.
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Silalahi, Desri Kristina. "Forecasting of Poverty Data Using Seasonal ARIMA Modeling in West Java Province." JTAM | Jurnal Teori dan Aplikasi Matematika 4, no. 1 (April 24, 2020): 76. http://dx.doi.org/10.31764/jtam.v4i1.1888.
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The government continues to carry out poverty reduction strategies in Indonesia, especially in West Java Province. West Java Province is a province that has the most populous population in Indonesia. This will affect the level of welfare and the amount of poverty. The strategy undertaken is inseparable from accurate poverty data and is available from year to year. Even from the available data, the government can forecast the number of poor people in the coming years. Seasonal Autoregressive Integrated Moving Average (SARIMA) method is one of forecasting methods. SARIMA is the development of the ARIMA model which has a seasonal effect. Based on the results of the study, that poverty data forecasting in the province of West Java using the SARIMA method obtained SARIMA model (0,1,1) (1,1,1)4. This model is the best model for forecasting data with an R-Squared value of 98%, Mean Square Error is 7.705.5800.000 and Mean Absolute Percentage Error IS 2,81%. It’s means this SARIMA model is very good in predicting poverty data in West Java Province.
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Salauddin Khan, Md, Masudul Islam, Sajal Adhikary, Md Murad Hossain, and Sohani Afroja. "Analysis and Predictions of Seasonal Affected Weather Variables of Bangladesh: SARIMA Models vs. Traditional Models." International Journal of Business and Management 13, no. 12 (November 12, 2018): 70. http://dx.doi.org/10.5539/ijbm.v13n12p70.
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Bangladesh is a semi-tropical country, categorized by widespread seasonal disparities in rainfall, temperature, and humidity. Seasonality has been an input aspect of time series modeling when taking into account weather variables. In terms of multiple features of the weather variables i.e. randomness, cyclical variation and trend, time series methods etc. ARIMA can be a superior preference but, weather variables are affected by seasonality. Thinking about the grimy meadow, this paper presents Seasonal Auto-regressive Moving Average (SARIMA) model that takes seasonal and cyclical variation over the years. This study also aims to compare traditional methods like Single Exponential Method, Double Exponential Method, and Holt Winter Method with the SARIMA model. Time series plots, month plots, and B-B plots are used for identifying seasonal effect clearly. For seasonal stationary checking, Canova Hansen Stationary test has been utilized. Then, the order of the variables is identified, ACF and PACF have been checked and estimated preeminent order for these variables by AIC and Log-likelihood. Finally, Single Exponential Method, Double Exponential Method, and Holt Winter Method are introduced for comparing and forecasting. The proposed models SARIMA(0,0,0)(1,0,3)12, SARIMA(0,0,0)(1,0,1)12, SARIMA(0,0,0)(1,0,2)12 and SARIMA(0,0,0)(1,0,1)12 for maximum and minimum temperature, rainfall and humidity on the basis of Akaike Information Criteria and Log likelihood have been captured most seasonality of the data. Comparing them with traditional methods, traditional methods give a better result than the acquired model based on error measurement. So, traditional methods give a better estimate than the SARIMA models for selected weather variables, with lower mean square error, RMSE, MAE and MASE.
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Wu, Don Chi Wai, Lei Ji, Kaijian He, and Kwok Fai Geoffrey Tso. "Forecasting Tourist Daily Arrivals With A Hybrid Sarima–Lstm Approach." Journal of Hospitality & Tourism Research 45, no. 1 (June 18, 2020): 52–67. http://dx.doi.org/10.1177/1096348020934046.
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Timely predicting tourist demand is extremely important for the tourism industry. However, due to limited availability of data, most of the relevant research studies have focused on data on a quarterly or monthly basis. In this article, we propose a novel hybrid approach, SARIMA + LSTM, that is, seasonal autoregressive integrated moving average (SARIMA) combined with long short-term memory (LSTM) to forecast daily tourist arrivals to Macau SAR, China. The LSTM model is a novel artificial intelligence nonlinear method which has been shown to have the capacity to learn the long-term dependencies existing in the time series. SARIMA + LSTM benefits from the predictive power of the SARIMA model and the ability of the LSTM to further reduce residuals. The results show that the SARIMA + LSTM forecast technique outperforms other methods.
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Multiningsih, Multiningsih, Emy Siswanah, and Minhayati Saleh. "Forecasting the Number of Ship Passengers with SARIMA Approach (A Case Study: Semayang Port, Balikpapan City)." JTAM (Jurnal Teori dan Aplikasi Matematika) 6, no. 4 (October 8, 2022): 1060. http://dx.doi.org/10.31764/jtam.v6i4.10211.
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From year to year, the number of ship passengers at Semayang Port, Balikpapan city tends to fluctuate. It also doubles in certain months and repeats every year. Sea transportation companies need to make forecasts in order to implement policies related to predict the number and capacity of ships that need to be provided as well as the preparation of port facilities. The study aims at obtaining the best model, predicting and determining the accuracy of the forecasting results for the number of passengers arriving and departing at Semayang Port, Balikpapan city using SARIMA method. The SARIMA method is a time series data forecasting method that is able to identify seasonal patterns. The results showed that the best model for predicting the number of passengers departing at Semayang Port, Balikpapan city is the SARIMA (4,1,0)(0,1,2)12 model with a MAPE of 14.05%. It means that the SARIMA model used produces good forecasting. Meanwhile, the best model to predict the number of passengers coming to Semayang Port Balikpapan city is the SARIMA (0,1,1)(2,1,0)12 model with a MAPE value of 3.27% which exposes that the SARIMA model used succeed to provide accurate forecasting. The results of this forecast can be used as a reference for the government or port managers to anticipate a surge in passengers. The government or port management can prepare an adequate amount of transportation in certain months to avoid the accumulation of passengers and to make sea transportation more efficient.
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Wang, Yongbin, Chunjie Xu, Zhende Wang, and Juxiang Yuan. "Seasonality and trend prediction of scarlet fever incidence in mainland China from 2004 to 2018 using a hybrid SARIMA-NARX model." PeerJ 7 (January 17, 2019): e6165. http://dx.doi.org/10.7717/peerj.6165.
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Background Scarlet fever is recognized as being a major public health issue owing to its increase in notifications in mainland China, and an advanced response based on forecasting techniques is being adopted to tackle this. Here, we construct a new hybrid method incorporating seasonal autoregressive integrated moving average (SARIMA) with a nonlinear autoregressive with external input(NARX) to analyze its seasonality and trend in order to efficiently prevent and control this re-emerging disease. Methods Four statistical models, including a basic SARIMA, basic nonlinear autoregressive (NAR) method, traditional SARIMA-NAR and new SARIMA-NARX hybrid approaches, were developed based on scarlet fever incidence data between January 2004 and July 2018 to evaluate its temporal patterns, and their mimic and predictive capacities were compared to discover the optimal using the mean absolute percentage error, root mean square error, mean error rate, and root mean square percentage error. Results The four preferred models identified were comprised of the SARIMA(0,1,0)(0,1,1)12, NAR with 14 hidden neurons and five delays, SARIMA-NAR with 33 hidden neurons and five delays, and SARIMA-NARX with 16 hidden neurons and 4 delays. Among which presenting the lowest values of the aforementioned indices in both simulation and prediction horizons is the SARIMA-NARX method. Analyses from the data suggested that scarlet fever was a seasonal disease with predominant peaks of summer and winter and a substantial rising trend in the scarlet fever notifications was observed with an acceleration of 9.641% annually, particularly since 2011 with 12.869%, and moreover such a trend will be projected to continue in the coming year. Conclusions The SARIMA-NARX technique has the promising ability to better consider both linearity and non-linearity behind scarlet fever data than the others, which significantly facilitates its prevention and intervention of scarlet fever. Besides, under current trend of ongoing resurgence, specific strategies and countermeasures should be formulated to target scarlet fever.
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Fatima, Noor, Aamir Alamgir, and Moazzam Ali Khan. "Rainfall forecast using SARIMA model along the coastal areas of Sindh Province." International Journal of Economic and Environmental Geology 13, no. 4 (December 23, 2022): 35–41. http://dx.doi.org/10.46660/ijeeg.v13i4.51.
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Rainfall forecasting is critical for economic activities such as agriculture, watershed management, and flood control. It requires mathematical modelling and simulation. This paper investigates the time series analysis and forecasting of the monthly rainfall for the Sindh coastline, Pakistan. The seasonal autoregressive integrated moving average (SARIMA) model was used for the last three decades (1991-2020) and forecasting was done for the next two years. The model is based on the Box Jenkins methodology. The decomposition of time series plots into trend, seasonal and random components showed a seasonal effect. The Augmented Dickey–Fuller (ADF) and Mann–Kendall (MK) tests showed the inherent stationarity of the rainfall data. The best SARIMA models for monthly rainfall were SARIMA (1,0,1)(3,1,1)12 and SARIMA (1,0,1)(1,1,1)12 with Akaike information criterion corrected (AICC) values of 1507 and 1387, respectively. The model predictions indicate that, in the years 2021/22, July will likely have the most rainfall, followed by August and June. The diagnostic statistical test values directed that the adequacy of the models is consistent for projected monthly rainfall forecasts.
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Luo, Zixiao, Xiaocan Jia, Junzhe Bao, Zhijuan Song, Huili Zhu, Mengying Liu, Yongli Yang, and Xuezhong Shi. "A Combined Model of SARIMA and Prophet Models in Forecasting AIDS Incidence in Henan Province, China." International Journal of Environmental Research and Public Health 19, no. 10 (May 12, 2022): 5910. http://dx.doi.org/10.3390/ijerph19105910.
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Acquired immune deficiency syndrome (AIDS) is a serious public health problem. This study aims to establish a combined model of seasonal autoregressive integrated moving average (SARIMA) and Prophet models based on an L1-norm to predict the incidence of AIDS in Henan province, China. The monthly incidences of AIDS in Henan province from 2012 to 2020 were obtained from the Health Commission of Henan Province. A SARIMA model, a Prophet model, and two combined models were adopted to fit the monthly incidence of AIDS using the data from January 2012 to December 2019. The data from January 2020 to December 2020 was used to verify. The mean square error (MSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) were used to compare the prediction effect among the models. The results showed that the monthly incidence fluctuated from 0.05 to 0.50 per 100,000 individuals, and the monthly incidence of AIDS had a certain periodicity in Henan province. In addition, the prediction effect of the Prophet model was better than SARIMA model, the combined model was better than the single models, and the combined model based on the L1-norm had the best effect values (MSE = 0.0056, MAE = 0.0553, MAPE = 43.5337). This indicated that, compared with the L2-norm, the L1-norm improved the prediction accuracy of the combined model. The combined model of SARIMA and Prophet based on the L1-norm is a suitable method to predict the incidence of AIDS in Henan. Our findings can provide theoretical evidence for the government to formulate policies regarding AIDS prevention.
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Hirata, Enna, and Takuma Matsuda. "Forecasting Shanghai Container Freight Index: A Deep-Learning-Based Model Experiment." Journal of Marine Science and Engineering 10, no. 5 (April 27, 2022): 593. http://dx.doi.org/10.3390/jmse10050593.
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With the increasing availability of large datasets and improvements in prediction algorithms, machine-learning-based techniques, particularly deep learning algorithms, are becoming increasingly popular. However, deep-learning algorithms have not been widely applied to predict container freight rates. In this paper, we compare a long short-term memory (LSTM) method and a seasonal autoregressive integrated moving average (SARIMA) method for forecasting the comprehensive and route-based Shanghai Containerized Freight Index (SCFI). The research findings indicate that the LSTM deep learning models outperformed SARIMA models in most of the datasets. For South America and the east coast of the U.S. routes, LSTM could reduce forecasting errors by as much as 85% compared to SARIMA. The SARIMA models performed better than LSTM in predicting freight movements on the west and east Japan routes. The study contributes to the literature in four ways. First, it presents insights for improving forecasting accuracy. Second, it helps relevant parties understand the trends of container freight markets for wiser decision-making. Third, it helps relevant stakeholders understand overall container shipping market trends. Lastly, it can help hedge against the volatility of freight rates.
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Yahya, Irma, Mukhsar, Agusrawati, Lilis Laome, Andi Tenriawaru, Ida Usman, and Sitti Wirdhana Ahmad. "SARIMA model for compiling the planting calendar." Journal of Physics: Conference Series 1899, no. 1 (May 1, 2021): 012109. http://dx.doi.org/10.1088/1742-6596/1899/1/012109.
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Muthu, N. Sonai, K. Senthamarai Kannan, V. Deneshkumar, and P. Thangasamy. "SARIMA Model for Forecasting Price Indices Fluctuations." European Journal of Mathematics and Statistics 2, no. 6 (November 8, 2021): 1–6. http://dx.doi.org/10.24018/ejmath.2021.2.6.67.
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In day-to-day life, the price level fluctuations in the Consumer Price Index (CPI) goods and service. So, the retail consumers are affecting by that price level changes, who are on the demand side of the economy. The main objective of this work is to forecast such selected factors of CPI in urban and rural areas of India, like: Food and Beverages, Pan, Tobacco and Intoxicants, Fuel and Light and Education and also compute the inflation rate for those four main variables in all India.
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Zukhronah, Etik, Winita Sulandari, Isnandar Slamet, Sugiyanto Sugiyanto, and Irwan Susanto. "Model Variasi Kalender pada Regresi Runtun Waktu untuk Peramalan Jumlah Pengunjung Grojogan Sewu." Indonesian Journal of Applied Statistics 4, no. 2 (November 29, 2021): 67. http://dx.doi.org/10.13057/ijas.v4i2.47163.
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<p><strong>Abstract.</strong> Grojogan Sewu visitors experience a significant increase during school holidays, year-end holidays, and also Eid al-Fitr holidays. The determination of Eid Al-Fitr uses the Hijriyah calendar so that the occurrence of Eid al-Fitr will progress 10 days when viewed from the Gregorian calendar, this causes calendar variations. The objective of this paper is to apply a calendar variation model based on time series regression and SARIMA models for forecasting the number of visitors in Grojogan Sewu. The data are Grojogan Sewu visitors from January 2009 until December 2019. The results show that time series regression with calendar variation yields a better forecast compared to the SARIMA model. It can be seen from the value of root mean square error (<em>RMSE</em>) out-sample of time series regression with calendar variation is less than of SARIMA model.</p><p><strong>Keywords: </strong>Calendar variation, time series regression, SARIMA, Grojogan Sewu</p>
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Aulia, Fadila, Hazmira Yozza, and Dodi Devianto. "PERAMALAN CURAH HUJAN BULANAN KABUPATEN TANAH DATAR DENGAN MODEL SEASONAL AUTOREGRESSIVE INTEGRATED MOVING AVERAGE (SARIMA)." Jurnal Matematika UNAND 8, no. 2 (July 15, 2019): 37. http://dx.doi.org/10.25077/jmu.8.2.37-44.2019.
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Curah hujan merupakan salah satu faktor penting yang mempengaruhi berbagai aspek kehidupan terutama dalam bidang pertanian. Mengetahui besarnya curah hujan untuk waktu yang akan datang dapat membantu proses perencanaan manusia dalam berbagai aspek dan dapat menanggulangi berbagai permasalahan yang timbul di kemudian hari. Besarnya curah hujan untuk waktu yang akan datang dapat diprediksi dengan melakukan proses peramalan. Data curah hujan merupakan suatu data runtun waktu. Proses peramalan data runtun waktu dapat dilakukan dengan berbagai metode, salah satunya dengan menggunakan model Seasonal Autoregressive Integrated Moving Average (SARIMA). Pada penelitian ini dilakukan proses peramalan curah hujan bulanan kabupaten Tanah Datar dengan menggunakan data curah hujan bulanan kabupaten Tanah Datar dari bulan Januari 2013 sampai bulan Oktober 2018 dan diperoleh model SARIMA terbaik yaitu SARIMA(0, 0, 1)(0, 1, 0)6 . Berdasarkan hasil peramalan yang diperoleh, besarnya curah hujan kabupaten Tanah Datar untuk bulan Desember 2018 sampai bulan April 2019 konstan.Kata Kunci: Curah Hujan, Peramalan, SARIMA
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Adnan, Rana Muhammad, Xiaohui Yuan, Ozgur Kisi, and Yanbin Yuan. "Streamflow forecasting of Astore River with Seasonal Autoregressive Integrated Moving Average model." European Scientific Journal, ESJ 13, no. 12 (April 30, 2017): 145. http://dx.doi.org/10.19044/esj.2017.v13n12p145.
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Simulation of streamflow is one of important factors in water utilization. In this paper, a linear statistical model i.e. Seasonal Autoregressive Integrated Moving Average model (SARIMA) is applied for modeling streamflow data of Astore River (1974 – 2010). On the basis of minimum Akaike Information Criteria Corrected (AICc) and Bayesian Information Criteria (BIC) values, the best model from different model structures has been identified. For testing period (2004-2010), the prediction accuracy of selected SARIMA model in comparison of auto regressive (AR) is evaluated on basis of root mean square error (RMSE), the mean absolute error (MAE) and coefficient of determination (R2 ). The results show that SARIMA performed better than AR model and can be used in streamflow forecasting at the study site.