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Journal articles on the topic 'SARIMA model'

Author: Grafiati

Published: 4 June 2021

Last updated: 26 July 2025

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1

Ibrahim, A., and A. O. Musa. "ON THE PERFORMANCE OF SARIMA AND SARIMAX MODEL IN FORECASTING MONTHLY AVERAGE RAINFALL IN KOGI STATE, NIGERIA." FUDMA JOURNAL OF SCIENCES 7, no. 6 (2023): 24–31. http://dx.doi.org/10.33003/fjs-2023-0706-2095.

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Forecasting monthly rainfall is very important in Kogi state for better approach to flood management and also plays a pivotal role in agriculture which remains a significant factor in Nigeria’s economy. Advanced time series univariate models such as Seasonal Autoregressive Integrated Moving Average (SARIMA) models are usually employed in modelling and forecasting rainfall in Nigeria due to their non-linear pattern and spatiotemporal variation. Few studies have attempted to investigate the influence of other climatic factors in modelling and prediction of rainfall pattern. This study examines t

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Permata, Regita Putri, and Ananda Taqhsya Dwiyana. "Comparison of NAIVE, SARIMA, and SARIMAX Models in Short-Term and Long-Term Forecasting of Google Search Trends." International Journal of Scientific Research in Computer Science and Engineering 13, no. 2 (2025): 21–29. https://doi.org/10.26438/ijsrcse.v13i2.665.

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This study examines forecasting methods—Naïve, SARIMA, and SARIMAX—to enhance the accuracy of Google search trend predictions in the fitness industry, specifically for the keyword "Gym." The Naïve method serves as a baseline comparison, SARIMA incorporates seasonal, autoregressive, and moving average components for improved trend detection, while SARIMAX extends SARIMA by integrating exogenous variables. Historical Google search data from 2005 to 2015 is used for model evaluation, with performance assessed using MAE, RMSE, and MAPE metrics. The findings indicate that SARIMAX, which accounts fo

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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 (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

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Costa, Gabriela Emiliana de Melo e., Frederico Carlos M. de Menezes Filho, Fausto A. Canales, Maria Clara Fava, Abderraman R. Amorim Brandão, and Rafael Pedrollo de Paes. "Assessment of Time Series Models for Mean Discharge Modeling and Forecasting in a Sub-Basin of the Paranaíba River, Brazil." Hydrology 10, no. 11 (2023): 208. http://dx.doi.org/10.3390/hydrology10110208.

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Stochastic modeling to forecast hydrological variables under changing climatic conditions is essential for water resource management and adaptation planning. This study explores the applicability of stochastic models, specifically SARIMA and SARIMAX, to forecast monthly average river discharge in a sub-basin of the Paranaíba River near Patos de Minas, MG, Brazil. The Paranaíba River is a vital water source for the Alto Paranaíba region, serving industrial supply, drinking water effluent dilution for urban communities, agriculture, fishing, and tourism. The study evaluates the performance of SA

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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 (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. Pa

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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 (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

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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 (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 variab

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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 simil

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Kadek, Jemmy Waciko, and B. Ismail. "SARIMA-ELM Hybrid Model for Forecasting Tourist in Nepal." RESEARCH REVIEW International Journal of Multidisciplinary 03, no. 07 (2018): 343–49. https://doi.org/10.5281/zenodo.1318551.

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In this study a novel hybrid model has been developed to forecasting tourist arrivals. The main concept is to combine two different forecasting techniques such as SARIMA and Extreme Learning Machine models to produce a new SARIMA-ELM hybrid Model, so as to achieve accuracy in forecasting. Forecasting accuracy for SARIMA, Triple Exponential Smoothing (The Holt-Winter’s), Multi Layer Perceptron-Neural Networks (MLP-NN), Extreme Learning Machine (ELM) and SARIMA-ELM hybrid models are computed and compared using criteria like RMSE, MAE, and MAPE. Empirical analysis found that SARIMA-ELM hybr

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Yadav, Baikunth Kumar, Sunil Kumar Srivastava, Ponnusamy Thillai Arasu, and Pranveer Singh. "Time Series Modeling of Tuberculosis Cases in India from 2017 to 2022 Based on the SARIMA-NNAR Hybrid Model." Canadian Journal of Infectious Diseases and Medical Microbiology 2023 (December 16, 2023): 1–9. http://dx.doi.org/10.1155/2023/5934552.

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Tuberculosis (TB) is still one of the severe progressive threats in developing countries. There are some limitations to social and economic development among developing nations. The present study forecasts the notified prevalence of TB based on seasonality and trend by applying the SARIMA-NNAR hybrid model. The NIKSHAY database repository provides monthly informed TB cases (2017 to 2022) in India. A time series model was constructed based on the seasonal autoregressive integrated moving averages (SARIMA), neural network autoregressive (NNAR), and, SARIM-NNAR hybrid models. These models were es

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Panjala, Mounika, Vaishanavi Nampally, and Bhatracharyulu N. Ch. "Model Building for Air Traffic Flow with a Mixture Model." INTERNATIONAL JOURNAL OF AGRICULTURAL AND STATISTICAL SCIENCES 21, no. 01 (2025): 87. https://doi.org/10.59467/ijass.2025.21.87.

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Forecasting of air traffic is crucial in the study of airlines. The air traffic has increased steadily during the last decade. The number of passengers travelled through Indigo is collected from civil aviation to build an appropriate time series model for air traffic of Indigo. An attempt is made to fit the data using seasonal auto-regressive integrated moving average (SARlMA), Feed Forward Neural Network (FFNN) and Long Short Term Memory (LSTM) and a mixture of these models with proportionate efficiencies as weights is evaluated to predict the air traffic.. KEYWORDS :Civil Aviation, SARIMA, F

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Ali, Nasir, Muhammad Ali, and Hassan Hashim. "PREDICTIVE MODEL TO MINIMIZE THE EFFECT OF EXTREME TEMPERATURE IN SKARDU AND ASTORE, GILGIT BALTISTAN." Journal of Mountain Area Research 9 (June 29, 2024): 138. http://dx.doi.org/10.53874/jmar.v9i0.192.

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Climate is a fundamental factor of the natural environment that has a role in both natural and human existence. Temperature is an important climatic element that influences snow melting, evaporation, and frost directly. Current study has used Mean Monthly Minimum Temperature (MMMT) of Skardu from 1972 to 2021 and of Astore from 1993 to 2021 based on the availability of data. In this work; we have used SARIMA (Seasonal Auto Regressive Integrated Moving Average Model) to forecast mean monthly minimum temperature. Skardu data is stationary at level form, which suggests SARMA model for Skardu stat

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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 (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 tre

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Damor, P. A., A. A. Mod, Bhavin Ram, and H. V. Parmar. "Time series analysis and development of simulation model for monthly rainfall using ARIMA model." INTERNATIONAL JOURNAL OF AGRICULTURAL SCIENCES 20, no. 1 (2024): 226–35. http://dx.doi.org/10.15740/has/ijas/20.1/226-235.

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Rainfall holds critical significance for water resource applications, particularly in rainfed agricultural systems. This study employs the Autoregre ssive Integrated Moving Average (ARIMA) technique, a data mining approach commonly used for time series analysis and future forecasting. Given the increasing importance of climate change forecasting in averting unexpected natural hazards such as floods, frost, forest fires, and droughts, accurate weather data forecasting becomes imperative. The objective of this study was to develop a Seasonal Auto-Regressive Integrative Moving Average (SARIMA) mo

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Hidayatullah, M. Pio, Ferra Yanuar, and Dodi Devianto. "PEMODELAN JUMLAH PENUMPANG PESAWAT DI BANDARA SOEKARNO-HATTA MENGGUNAKAN MODEL HYBRiD SARIMA-FTSMC." Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika 4, no. 3 (2023): 1744–55. http://dx.doi.org/10.46306/lb.v4i3.513.

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The aim of this research is to model the number of airplane passengers at Soelkarno-Hatta airport using a model hybrid SARIMA-FTSMC. The data used in this research is secondary data in the form of data on the number of airplane passengers at Soekarno-Hatta airport from January 2010 to May 2023 which was obtained via the websitebps.go.id. After analyzing the data, the best model was obtained in the SARIMA model, namely SARIMA(1,1,1)(0,1,1)12. Then based on the residual value from the processed data SARIMA will be modeled using the FTSMC model. Next, the residual value of SARIMA(1,1,1)(0,1,1)12

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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 (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 ARI

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OGUNDEJI, ROTIMI, and Sherif Sunday Okemakinde. "Bayesian Structural Time Series Model and SARIMA Model for Rainfall Forecasting in Nigeria." Journal of Statistical Modelling and Analytics 7, no. 1 (2025): 54–67. https://doi.org/10.22452/josma.vol7no1.5.

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Nigeria is recognized as being susceptible to climate change, and global warming if not taken care of, will lead to serious problems on livelihoods in Nigeria, especially in the area of agricultural activities. Rainfall is a major determinant of climate change the world over and climate change is one of the foremost global challenge facing humans at the moment. Using monthly time series rainfall data, Bayesian structural time series (BSTS) methodology was applied to fit models through MCMC algorithm. Also, Seasonal Autoregressive Moving Average (SARIMA) models were fitted to the same dataset u

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Zhao, Daren, and Ruihua Zhang. "A new hybrid model SARIMA-ETS-SVR for seasonal influenza incidence prediction in mainland China." Journal of Infection in Developing Countries 17, no. 11 (2023): 1581–90. http://dx.doi.org/10.3855/jidc.18037.

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Introduction: Seasonal influenza is a serious public health issue in China. This study aimed to develop a new hybrid model for seasonal influenza incidence prediction and provide reference information for early warning management before outbreaks. Methodology: Data on the monthly incidence of seasonal influenza between 2004 and 2018 were obtained from the China Public Health Science Data Center website. A single seasonal autoregressive integrated moving average (SARIMA) model and a single error trend and seasonality (ETS) model were built. On this basis, we constructed SARIMA, ETS, and support

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Shrikant, G. V. Srinivasa Reddy, M. K. Manjunath, Rahul Patil, and Prasad S. Kulkarni. "Predicting Potential Evapotranspiration for Kalaburagi District using a Seasonal Arima Model." International Journal of Environment and Climate Change 13, no. 11 (2023): 2073–82. http://dx.doi.org/10.9734/ijecc/2023/v13i113367.

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Forecasting potential evapotranspiration (PET) is of great importance in effectively managing irrigation systems. This article centers around models designed to simulate future PET levels for the Kalaburagi district. The study calculates potential evapotranspiration using temperature data in degrees Celsius, employing the Thornthwaite method, and prediction is performed using the Seasonal Autoregressive Moving Average (SARIMA) method. These models are developed based on autocorrelation function (ACF) and partial autocorrelation function (PACF) analysis. Model selection is based on minimizing A

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Bunnag, Tanattrin. "Forecasting PM10 Caused by Bangkok’s Leading Greenhouse Gas Emission Using the SARIMA and SARIMA-GARCH Model." International Journal of Energy Economics and Policy 14, no. 1 (2024): 418–26. http://dx.doi.org/10.32479/ijeep.15275.

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This paper analyzes the relationship between air pollutants and the amount of PM10 measured in Bangkok. It forecasts the amount of PM10 in Bangkok by using the SARIMA and SARIMA-GARCH models to formulate policies to reduce the occurrence of PM10 and guidelines for further prevention. PM's data is from January 2008 to July 2023. First, the process is to build the SARIMA Model and SARIMA-GARCH Model Estimation. We perform model comparisons that SARIMA (3,1,3)(1,1,2)12 and SARIMA(3,1,3)(1,1,2)12-GARCH(1,1), which model gives lower MAE and RMSE values, which indicates good prediction accuracy than

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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 (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 Integr

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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 (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 co

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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 (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 (A

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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 (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

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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 (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 No

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Wan Amir, Wan Anis Farhah, and Md Yushalify Misro. "Improving Covid-19 Forecasts in Malaysia: A Hybrid SARIMAX-SARIMA Model with Application to State Elections and Cultural Festivals." Malaysian Journal of Fundamental and Applied Sciences 20, no. 6 (2024): 1478–92. https://doi.org/10.11113/mjfas.v20n6.3606.

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Since the onset of the Covid-19 pandemic, numerous challenges have emerged, including ensuring an adequate supply of personal protective equipment, evaluating the sufficiency of the healthcare workforce, and determining safety measures to sustain businesses and the economy. Consequently, there is a critical need for a computationally competent and realistic model to monitor current caseloads and forecast future cases, thereby enhancing public health awareness, preparation, and response. However, many forecast models currently in use have wide prediction intervals, diminishing their effectivene

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Yeasin, Md, K. N. Singh, Ramasubramanian V, Ranjit Kumar Paul, and Achal Lama. "Application of SARIMA model for precipitation modelling driven by exogenous variables." MAUSAM 76, no. 2 (2025): 365–72. https://doi.org/10.54302/mausam.v76i2.5487.

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The Seasonal Autoregressive Integrated Moving Average (SARIMA) model has gained popularity since its inception due to its ability to forecast seasonality. Usually, the SARIMA model captures the seasonality but does not consider the effect of the exogenous variable(s) in the seasonality process. Hence, this study aims to empirically introduce and implement the SARIMA-X model which can account for seasonality as well as the effects of influencing factors (X). Climate change has become the foremost global challenge facing human existence and the effect will be multifaceted with respect to social,

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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 (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 carri

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Fortuna, Hilda Najwa Dewi, and Affiati Oktaviarina. "Metode SARIMA ARCH PERAMALAN JUMLAH PRODUKSI PADI KABUPATEN NGAWI MENGGUNAKAN METODE SARIMA ARCH." MATHunesa: Jurnal Ilmiah Matematika 12, no. 2 (2024): 418–27. https://doi.org/10.26740/mathunesa.v12n2.p418-427.

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Seasonal Autoregressive Integrated Moving Average (SARIMA) adalah metode peramalan time series untuk model data fluktuatif dengan pola data musiman. Model Autoregressive Conditional Heteroskedastisitas (ARCH) adalah model yang berfungsi untuk mengatasi masalah heteroskedastisitas atau varians redisual dalam data time series. Salah satu implementasi dari model SARIMA ARCH yaitu untuk meramalkan jumlah produksi padi Kabupaten Ngawi. Data yang digunakan dalam penelitian ini adalah data bulanan pada bulan Januari 2019 sampai dengan Maret 2023. Hasil dari penelitian ini diperoleh model SARIMA (1,1,

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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 (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 su

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Silfiani, Mega. "MODEL GABUNGAN (ANSAMBEL) SARIMA DAN JARINGAN SARAF TIRUAN UNTUK PERAMALAN BEBAN LISTRIK." VARIANCE: Journal of Statistics and Its Applications 5, no. 2 (2023): 193–200. http://dx.doi.org/10.30598/variancevol5iss2page193-200.

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This study aims to investigate the efficacy of employing artificial neural networks in conjunction with a seasonal autoregressive integrated moving average (SARIMA) ensemble for forecasting electrical load. The SARIMA ensemble comprises members generated by varying autoregressive orders or moving averages. Subsequently, these SARIMA ensemble members are integrated using artificial neural networks. The datasets encompass monthly electrical load data pertaining to households, businesses, industries, and the public, spanning from January 2016 to December 2020. The findings demonstrate that across

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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 (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. Af

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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 (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 fi

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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 (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 fi

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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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Liu, Dongyao. "The prediction and analysis of global climate change based on SARIMA." Applied and Computational Engineering 40, no. 1 (2024): 268–73. http://dx.doi.org/10.54254/2755-2721/40/20230665.

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Global climate change is a significant challenge that the world is currently facing. Accurate prediction of global climate change is essential for environmental protection, agricultural production, and social development. This study explores the utilization of the Seasonal Autoregressive Integrated Moving Average (SARIMA) model for forecasting global climate change. The SARIMA model is a machine learning algorithm that can effectively capture seasonal patterns and non-linear characteristics of climate data. The study initiates by performing data preprocessing tasks, which encompass data cleani

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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 (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 probl

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Reyes Reyes, José Luis, Guillermo Urriolagoitia Sosa, Francisco Javier Gallegos Funes, Beatriz Romero Ángeles, Israel Flores Baez, and Misael Flores Baez. "Statistical Analysis and SARIMA Forecasting Model Applied to Electrical Energy Consumption in University Facilities." Científica 26, no. 2 (2022): 1–22. http://dx.doi.org/10.46842/ipn.cien.v26n2a03.

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Analyzing the energy consumption behavior in buildings is essential for implementing energy-saving and efficient energy use measures without losing attention to the comfort inside the buildings. In this study, a statistical analysis and time series forecast of the energy situation of a group of buildings in a university academic unit in Mexico City was conducted. Seasonal Autoregressive Integrated Moving Average (SARIMA) models were used for the forecast with electrical energy consumption data from 55 months. Training and test partitions were created with these data to generate two SARIMA mode

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Eryürük, Şule. "Bir Tarım Makineleri Üreticisi için SARIMA Modeli ile Tahminleme." Osmaniye Korkut Ata Üniversitesi Fen Bilimleri Enstitüsü Dergisi 8, no. 3 (2025): 1146–68. https://doi.org/10.47495/okufbed.1549538.

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Tarım makineleri üretiminde karşılaşılan en önemli riskler günümüzde iklim değişikliğinden kaynaklı talep zamanlarında ve miktarlarında kayma ve rekabet unsurları olarak değerlendirilebilir. Bu nedenle tarım makineleri üretiminde istatistiksel talep tahmini yapmak elzem bir konu olmuştur. Bu çalışmanın amacı, tarım makineleri sektöründe bir imalatçıdan elde edilen 2011-2021 yılları arasındaki aylık üretim verilerinden yararlanarak gelecek 12 aylık dönemde üreticinin ürettiği en önemli iki ürününe ait üretim miktarlarını tahmin etmek ve gelecek üretim adetlerine dair öneriler geliştirmektir. Ta

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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 (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 decis

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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) (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),

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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 (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),

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Simanjuntak, Humasak Tommy Argo, Amelia Lumbanraja, Gabriel Samosir, and Regita. "Prediksi Single-Step dan Multi-Step Data Cuaca Menggunakan Model Long Short-Term Memory dan Sarima." Jurnal Teknologi Informasi dan Ilmu Komputer 12, no. 2 (2025): 399–410. https://doi.org/10.25126/jtiik.1229444.

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Prediksi deret waktu pada parameter data cuaca adalah proses memprediksi nilai masa depan berdasarkan pola data historis cuaca. Penelitian ini mengatasi kelemahan penelitian sebelumnya seperti data yang terbatas, jangka waktu prediksi, keterbatasan parameter yang digunakan dalam penelitian serta tidak menggunakan parameter eksternal yang tentunya dapat membantu proses prediksi model menjadi lebih akurat. Penelitian ini menggunakan metode Long Short-Term Memory (LSTM) dan Seasonal AutoRegressive Integrated Moving Average (SARIMA) untuk memprediksi parameter cuaca, seperti tekanan udara, suhu, d

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Simanjuntak, Humasak Tommy Argo, Amelia Lumbanraja, Gabriel Samosir, and Regita. "Prediksi Single-Step dan Multi-Step Data Cuaca Menggunakan Model Long Short-Term Memory dan Sarima." Jurnal Teknologi Informasi dan Ilmu Komputer 12, no. 2 (2025): 399–410. https://doi.org/10.25126/jtiik.2025129444.

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Prediksi deret waktu pada parameter data cuaca adalah proses memprediksi nilai masa depan berdasarkan pola data historis cuaca. Penelitian ini mengatasi kelemahan penelitian sebelumnya seperti data yang terbatas, jangka waktu prediksi, keterbatasan parameter yang digunakan dalam penelitian serta tidak menggunakan parameter eksternal yang tentunya dapat membantu proses prediksi model menjadi lebih akurat. Penelitian ini menggunakan metode Long Short-Term Memory (LSTM) dan Seasonal AutoRegressive Integrated Moving Average (SARIMA) untuk memprediksi parameter cuaca, seperti tekanan udara, suhu, d

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Febrian, M. Yandre, and Arie Wahyu Wijayanto. "Prediksi Jumlah Wisatawan Mancanegara Yang Masuk Melalui Bandara Kualanamu Menggunakan Big Data Google Trends." Seminar Nasional Official Statistics 2024, no. 1 (2024): 851–62. http://dx.doi.org/10.34123/semnasoffstat.v2024i1.2273.

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Jumlah wisatawan mancanegara yang terus meningkat di Sumatera Utara membuat pemerintah harus mempersiapkan strategi yang tepat untuk kebijakan yang diambil. Perilisan data yang dilakukan Badan Pusat Statistik (BPS) selaku lembaga yang bertanggung jawab masih memiliki kekurangan yaitu adanya gap waktu antara pengumpulan dan publikasi data. Penggunaan Google Trends sebagai data pendukung pengisi gap waktu tersebut dapat dilakukan karena data Google Trends yang dapat diakses secara real time. Penelitian ini bertujuan untuk melihat hubungan antara data Google Trends dengan data official statistics

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Wang, Yongbin, Chunjie Xu, Shengkui Zhang, et al. "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 (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 observati

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Khoiriyah, Nurhastivania Sohifatul, Mega Silfiani, Resti Novelinda, and Surya Muhammad Rezki. "Peramalan Jumlah Penumpang Kapal di Pelabuhan Balikpapan dengan SARIMA." Jurnal Statistika dan Komputasi 2, no. 2 (2023): 76–82. http://dx.doi.org/10.32665/statkom.v2i2.2303.

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Latar Belakang: Peramalan jumlah kedatangan penumpang kapal dalam negeri di pelabuhan dalam negeri sangat penting untuk antisipasi lonjakan penumpang. Tujuan: Tujuan dari penelitian ini adalah mendapatkan model terbaik untuk peramalan jumlah kedatangan penumpang kapal. Metode: Penelitian ini menggunakan metode Seasonal Autoregressive Integrated Moving Average (SARIMA). Data jumlah kedatangan penumpang kapal dalam negeri di Pelabuhan Balikpapan dari Januari 2017 sampai dengan Desember 2021. Root mean absolute error (RMSE) digunakan untuk membandingkan akurasi peramalan. Hasil: Model SARIMA yang

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Azad, Abdus Samad, Rajalingam Sokkalingam, Hanita Daud, et al. "Water Level Prediction through Hybrid SARIMA and ANN Models Based on Time Series Analysis: Red Hills Reservoir Case Study." Sustainability 14, no. 3 (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 R

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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 (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.) w

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RIZAL, MUHAMMAD, and Sri Utami Zuliana. "FORECASTING USING SARIMA AND BAYESIAN STRUCTURAL TIME SERIES METHOD FOR RANGE SEASONAL TIME." Proceedings of The International Conference on Data Science and Official Statistics 2023, no. 1 (2023): 382–91. http://dx.doi.org/10.34123/icdsos.v2023i1.402.

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Data containing seasonal patterns, the SARIMA and Bayesian Structural Time Series methods, are time series methods that can be used on this type of data. This research aims to determine the steps of the SARIMA model and Bayesian Structural Time Series, applying the SARIMA model and Structural Bayesians Time Series, get the forecasting results of the SARIMA model and Bayesian Structural Time Series with MAPE measurements. The research method used is a quantitative method applied to data on the number of PT KAI train passengers in the Java region for 2006-2019. The results of this research show

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