Journal articles: 'ARIMAX'

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Amelia, R., D. Y. Dalimunthe, E. Kustiawan, and I. Sulistiana. "ARIMAX model for rainfall forecasting in Pangkalpinang, Indonesia." IOP Conference Series: Earth and Environmental Science 926, no. 1 (November 1, 2021): 012034. http://dx.doi.org/10.1088/1755-1315/926/1/012034.

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Abstract In recent years, the weather and climate are unpredictable and the most visible is the rotation of the rainy season and the dry season. The extreme changes in rainfall can cause disasters and losses for the community. For that we need to predict the rainfall to anticipate the worst events. Rainfall is included in the periodic series data, so the forecasting method that can be used is the ARIMAX model which is ARIMA model expanded by adding the exogen variable. The aim of this research is to predict the rainfall data in Pangkalpinang City, Indonesia. The best model for each rainfall is ARIMAX (0,1,3) for monthly rainfall data and ARIMAX (0,1,2) for maximum daily rainfall. This research shows that there is an influence maximum wind speed variable to monthly rainfall and maximum daily rainfall in the Pangkalpinang City. Nevertheless, when viewed from the ARIMA and ARIMAX models based on the obtained AIC value, the ARIMAX value is still better than ARIMA. However, the prediction value using ARIMAX needs to increase again to take into account seasonal data rainfall. Then, possible to add other exogeneous factors besides maximum wind speed.

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Chen, Yun-Peng, Le-Fan Liu, Yang Che, Jing Huang, Guo-Xing Li, Guo-Xin Sang, Zhi-Qiang Xuan, and Tian-Feng He. "Modeling and Predicting Pulmonary Tuberculosis Incidence and Its Association with Air Pollution and Meteorological Factors Using an ARIMAX Model: An Ecological Study in Ningbo of China." International Journal of Environmental Research and Public Health 19, no. 9 (April 28, 2022): 5385. http://dx.doi.org/10.3390/ijerph19095385.

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The autoregressive integrated moving average with exogenous regressors (ARIMAX) modeling studies of pulmonary tuberculosis (PTB) are still rare. This study aims to explore whether incorporating air pollution and meteorological factors can improve the performance of a time series model in predicting PTB. We collected the monthly incidence of PTB, records of six air pollutants and six meteorological factors in Ningbo of China from January 2015 to December 2019. Then, we constructed the ARIMA, univariate ARIMAX, and multivariate ARIMAX models. The ARIMAX model incorporated ambient factors, while the ARIMA model did not. After prewhitening, the cross-correlation analysis showed that PTB incidence was related to air pollution and meteorological factors with a lag effect. Air pollution and meteorological factors also had a correlation. We found that the multivariate ARIMAX model incorporating both the ozone with 0-month lag and the atmospheric pressure with 11-month lag had the best performance for predicting the incidence of PTB in 2019, with the lowest fitted mean absolute percentage error (MAPE) of 2.9097% and test MAPE of 9.2643%. However, ARIMAX has limited improvement in prediction accuracy compared with the ARIMA model. Our study also suggests the role of protecting the environment and reducing pollutants in controlling PTB and other infectious diseases.

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ALKALI, MUSA ABUBAKAR. "ASSESSING THE FORECASTING PERFORMANCE OF ARIMA AND ARIMAX MODELS OF RESIDENTIAL PRICES IN ABUJA NIGERIA." Asia Proceedings of Social Sciences 4, no. 1 (April 17, 2019): 4–6. http://dx.doi.org/10.31580/apss.v4i1.528.

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This paper compared the out of sample forecasting ability of two Box-Jenkins ARIMA family models: ARIMAX and ARIMA. The forecasting models were tested to forecast real estate residential price in Abuja, Nigeria with quarterly data of average sales of residential price from the first quarter of year 2000 to the last quarter of year 2017. The result shows that the ARIMAX forecasting models, with macroeconomic factors as exogenous variables such as the household income, interest rate, gross domestic products, exchange rate and crude oil price and their lags, provide the best out of sample forecasting models for 2 bedroom, 3 bedroom, 4 bedroom and 5 bedroom, than ARIMA models. Generally, both ARIMA and ARIMAX models are good for short term forecasting modelling.

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Putera, Muhammad Luthfi Setiarno. "PERAMALAN TRANSAKSI PEMBAYARAN NON-TUNAI MENGGUNAKAN ARIMAX-ANN DENGAN KONFIGURASI KALENDER." BAREKENG: Jurnal Ilmu Matematika dan Terapan 14, no. 1 (March 1, 2020): 135–46. http://dx.doi.org/10.30598/barekengvol14iss1pp135-146.

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Akses internet yang luas mendorong kian seringnya sistem pembayaran non-tunai digunakan. Di Indonesia, berbagai aktivitas dan transaksi ekonomi seringkali dipengaruhi oleh pergerakan kalender, terutama kalender Hijriyah. Tujuan penelitian ini untuk memodelkan dan meramalkan total pembayaran non-tunai di Indonesia dengan menambahkan konfigurasi kalender sebagai variabel. Digunakan metode ARIMA, ARIMAX dan hibrida ARIMAX-ANN yang akan dibandingkan akurasinya. Diperoleh model terbaik untuk peramalan jumlah pembayaran non-tunai adalah ARIMAX-ANN dengan RMSE terkecil, yaitu Rp 20,9 triliun. Spesifikasi model terbaik tersebut adalah ARIMAX(2,1,1) yang dihibrida dengan ANN yang inputnya diseleksi melalui regresi stepwise. Selain memenuhi asumsi galat yang identik, independen, dan berdistribusi normal, ARIMAX-ANN juga mampu mengikuti dinamika dan tren dari pembayaran non-tunai, khususnya pada bulan jatuhnya Idul Fitri dan periode akhir tahun.

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TAMUKE, Edmund, Emerson Abraham JACKSON, and Abdulai SILLAH. "FORECASTING INFLATION IN SIERRA LEONE USING ARIMA AND ARIMAX: A COMPARATIVE EVALUATION. MODEL BUILDING AND ANALYSIS TEAM." Theoretical and Practical Research in the Economic Fields 9, no. 1 (June 30, 2018): 63. http://dx.doi.org/10.14505/tpref.v9.1(17).07.

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The study has provided empirical investigation of both ARIMA and ARIMAX methodology as a way of providing forecast of Headline Consumer Price Index (HCPI) for Sierra Leone based on data collected from the Sierra Leone Statistical Office and the Bank of Sierra Leone. In this, the main research question of addressing outcomes from in and out-of-sample forecast were provided using the Static technique and this shows that both methodologies were proved to have tracked past and future occurrences of HCPI with minimal margin of error as indicated in the MAPE results. In a similar note, the key objective of identifying whether the ARIMAX methodology or the ARIMA methodology is a better predictor of forecasting future trends in HCPI. However, on the whole, both ARIMA and ARIMAX seem to have provided very good outcome in predicting future events of HCPI, particularly when Static technique is used as the option for forecasting outcomes, with the ARIMAX marginally coming out as the preferred choice on the basis of its evaluation outcomes.

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Kurnia, Alma, and Ibnu Hadi. "Peramalan Nilai Ekspor Produk Industri Alas Kaki Menggnakan Model ARIMAX dengan Efek Variasi Kalender." Jurnal Statistika dan Aplikasinya 3, no. 2 (December 30, 2019): 25–34. http://dx.doi.org/10.21009/jsa.03204.

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Model ARIMAX adalah model ARIMA dengan peubah tambahan. Peubah tambahan yang digunakan untuk data deret waktu dengan variasi kalender berupa variabel dummy. Pada makalah ini, akan dilakukan penghitungan peramalan nilai ekspor produk industri alas kaki bulan Juli 2019 sampai dengan Jui 2020 dengan menggunakan model ARIMAX dengan efek variasi kalender. Efek variasi kalender yang ditemukan pada data nilai ekspor produk industri alas kaki adalah libur hari raya Idul Fitri. Data yang digunakan pada makalah ini yaitu data nilai ekspor produk industri alas kaki mulai dari bulan Januari tahun 2010 sampai dengan bulan Juni tahun 2019. Pemodelan ARIMAX dilakukan dengan menggabungkan model regresi dummy dari data aktual dan model ARIMA dari data residual.

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Bielak, Jarosław. "Prognozowanie rynku pracy woj. lubelskiego z wykorzystaniem modeli ARIMA i ARIMAX." Barometr Regionalny. Analizy i Prognozy, no. 1 (19) (May 13, 2010): 27–44. http://dx.doi.org/10.56583/br.1379.

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W artykule przedstawiono metodę prognozowania rynku pracy – poziomu bezrobocia i przeciętnego zatrudnienia – w woj. lubelskim w oparciu o modele ARIMA i ARIMAX. Dodatkowymi zmiennymi egzogenicznymi wprowadzanymi do standardowych modeli ARIMA były szeregi wartości indeksu nastrojów gospodarczych. Pokazano różnice we wskaźnikach charakteryzujących jakość prognoz generowanych przez model ARIMAX i „czysty” model ARIMA. Uwzględniono modele budowane dla danych kwartalnych i dla danych miesięcznych oraz omówiono sposób konwersji kwartalnych szeregów czasowych indeksu nastrojów gospodarczych do szeregów miesięcznych. Wykonano analizę weryfikującą rzeczywistą przydatność takiej metody prognozowania i korzyści płynące z jej stosowania.

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Rizalde, Fadlika Arsy, Sri Mulyani, and Nelayesiana Bachtiar. "Forecasting Hotel Occupancy Rate in Riau Province Using ARIMA and ARIMAX." Proceedings of The International Conference on Data Science and Official Statistics 2021, no. 1 (January 4, 2022): 578–89. http://dx.doi.org/10.34123/icdsos.v2021i1.199.

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Hotel Occupancy Rate is one of the important leading indicators for calculating the Accommodation Sub-Category of Gross Regional Domestic Product (GRDP). By the extreme decline of the Hotel Occupancy Rate data due to COVID-19 and the unavailability of current data to counting GRDP quarterly, the Hotel Occupancy Rate prediction needs to do with the appropriate forecasting method. The authors use data from Google Trends as an additional variable in predicting the Hotel Occupancy Rate using the ARIMAX model and then compares it with the ARIMA model. The results showed that the ARIMAX model had better accuracy than ARIMA, with a MAPE value of 9.64 percent and an RMSE of 4.21 percent. This research concluded that if there is no change in government policy related to social restrictions until the end of the year, the ARIMAX model predicts the December 2021 Hotel Occupancy Rate of 38.59 percent.

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Putera, Muhammad Luthfi Setiarno. "IMPROVISASI MODEL ARIMAX-ANFIS DENGAN VARIASI KALENDER UNTUK PREDIKSI TOTAL TRANSAKSI NON-TUNAI." Indonesian Journal of Statistics and Its Applications 4, no. 2 (July 31, 2020): 296–310. http://dx.doi.org/10.29244/ijsa.v4i2.603.

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Developed information technology boosts interest to use non-cash payment media in many areas. Following the high usage of a non-cash scheme in many payment transactions recently, the objective of this work is two-fold that is to predict the total of a non-cash transaction by using various time-series models and to compare the forecasting accuracy of those models. As a country with a mostly dense Moslem population, plenty of economical activities are arguably influenced by the Islamic calendar effect. Therefore the models being compared are ARIMA, ARIMA with Exogenous (ARIMAX), and a hybrid between ARIMAX and Adaptive Neuro-Fuzzy Inference Systems (ANFIS). By taking such calendar variation into account, the result shows that ARIMAX-ANFIS is the best method in predicting non-cash transactions since it produces lower MAPE. It is indicated that non-cash transaction increases significantly ahead of Ied Fitr occurrence and hits the peak in December. It demonstrates that the hybrid model can improve the accuracy performance of prediction.

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Diksa, I. Gusti Bagus Ngurah. "Forecasting the Existence of Chocolate with Variation and Seasonal Calendar Effects Using the Classic Time Series Approach." Jurnal Matematika, Statistika dan Komputasi 18, no. 2 (January 1, 2022): 237–50. http://dx.doi.org/10.20956/j.v18i2.18542.

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Chocolate is the raw material for making cakes, so consumption of chocolate also increases on Eid al-Fitr. However, this is different in the United States where the tradition of sharing chocolate cake is carried out on Christmas. To monitor the existence of this chocolate can be through the movement of data on Google Trends. This study aims to predict the existence of chocolate from the Google trend where the use of chocolate by the community fluctuates according to the calendar variance and seasonal rhythm. The method used is classic time series, namely nave, double exponential smoothing, multiplicative decomposition, addictive decomposition, holt winter multiplicative, holt winter addictive, time series regression, hybrid time series, ARIMA, and ARIMAX. Based on MAPE in sample, the best time series model to model the existence of chocolate in Indonesia is ARIMAX (1,0,0) while for the United States it is Hybrid Time Series Regression-ARIMA(2,1,[10]). For forecasting the existence of chocolate in Indonesia, the best models in forecasting are ARIMA (([11],[12]),1,1) and Naïve Seasonal. In contrast to the best forecasting model for the existence of chocolate in the United States, namely Hybrid Naïve Seasonal-SARIMA (2,1,0)(0,0,1)12 Hybrid Time Series Regression- ARIMA(2,1,[10]), Time Series Regression, Winter Multiplicative, ARIMAX([3],0,0).

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Zhang, Hong, Kun Su, and Xiaoni Zhong. "Association between Meteorological Factors and Mumps and Models for Prediction in Chongqing, China." International Journal of Environmental Research and Public Health 19, no. 11 (May 29, 2022): 6625. http://dx.doi.org/10.3390/ijerph19116625.

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(1) Background: To explore whether meteorological factors have an impact on the prevalence of mumps, and to make a short–term prediction of the case number of mumps in Chongqing. (2) Methods: K–means clustering algorithm was used to divide the monthly mumps cases of each year into the high and low case number clusters, and Student t–test was applied for difference analysis. The cross–correlation function (CCF) was used to evaluate the correlation between the meteorological factors and mumps, and an ARIMAX model was constructed by additionally incorporating meteorological factors as exogenous variables in the ARIMA model, and a short–term prediction was conducted for mumps in Chongqing, evaluated by MAE, RMSE. (3) Results: All the meteorological factors were significantly different (p < 0.05), except for the relative humidity between the high and low case number clusters. The CCF and ARIMAX model showed that monthly precipitation, temperature, relative humidity and wind velocity were associated with mumps, and there were significant lag effects. The ARIMAX model could accurately predict mumps in the short term, and the prediction errors (MAE, RMSE) were lower than those of the ARIMA model. (4) Conclusions: Meteorological factors can affect the occurrence of mumps, and the ARIMAX model can effectively predict the incidence trend of mumps in Chongqing, which can provide an early warning for relevant departments.

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Prendin, Francesco, José-Luis Díez, Simone Del Favero, Giovanni Sparacino, Andrea Facchinetti, and Jorge Bondia. "Assessment of Seasonal Stochastic Local Models for Glucose Prediction without Meal Size Information under Free-Living Conditions." Sensors 22, no. 22 (November 10, 2022): 8682. http://dx.doi.org/10.3390/s22228682.

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Accurate blood glucose (BG) forecasting is key in diabetes management, as it allows preventive actions to mitigate harmful hypoglycemic/hyperglycemic episodes. Considering the encouraging results obtained by seasonal stochastic models in proof-of-concept studies, this work assesses the methodology in two datasets (open-loop and closed-loop) recorded in free-living conditions. First, similar postprandial glycemic profiles are grouped together with fuzzy C-means clustering. Then, a seasonal stochastic model is identified for each cluster. Finally, real-time BG forecasting is performed by weighting each model’s prediction. The proposed methodology (named C-SARIMA) is compared to other linear and nonlinear black-box methods: autoregressive integrated moving average (ARIMA), its variant with input (ARIMAX), a feed-forward neural network (NN), and its modified version (NN-X) fed by BG, insulin, and carbohydrates (timing and dosing) information for several prediction horizons (PHs). In the open-loop dataset, C-SARIMA grants a median root-mean-squared error (RMSE) of 20.13 mg/dL (PH = 30) and 27.23 mg/dL (PH = 45), not significantly different from ARIMA and NN. Over a longer PH, C-SARIMA achieves an RMSE = 31.96 mg/dL (PH = 60) and RMSE = 33.91 mg/dL (PH = 75), significantly outperforming the ARIMA and NN, without significant differences from the ARIMAX for PH ≥ 45 and the NN-X for PH ≥ 60. Similar results hold on the closed-loop dataset: for PH = 30 and 45 min, the C-SARIMA achieves an RMSE = 21.63 mg/dL and RMSE = 29.67 mg/dL, not significantly different from the ARIMA and NN. On longer PH, the C-SARIMA outperforms the ARIMA for PH > 45 and the NN for PH > 60 without significant differences from the ARIMAX for PH ≥ 45. Although using less input information, the C-SARIMA achieves similar performance to other prediction methods such as the ARIMAX and NN-X and outperforming the CGM-only approaches on PH > 45min.

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Maya Sierra, Giuliana, and Nini Johana Marin Rodríguez. "Modelación y comovimientos de la tasa de cambio colombiana, 2011-2017." Revista de Métodos Cuantitativos para la Economía y la Empresa 28 (November 8, 2019): 301–41. http://dx.doi.org/10.46661/revmetodoscuanteconempresa.2966.

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La tasa de cambio está influenciada por múltiples factores macroeconómicos nacionales e internacionales, lo que genera altos niveles de incertidumbre. El objetivo de esta investigación es la construcción de modelos ARIMA-GARCH y ARIMAX-GARCH como herramienta para el pronóstico de la tasa de cambio en Colombia a partir de los retornos diarios de los precios de cierre USD/COP y su análisis de correlación dinámica con algunas variables de interés. Los resultados sugieren que la incorporación de variables exógenas significativas dentro de la modelación ARIMAX-GARCH con correlación persistente según el modelo DCC (por sus siglas en inglés Dinamic Conditional Correlation) al par USD/COP genera pronósticos fuera de muestra con mejor desempeño que los modelos univariados ARIMA-GARCH.

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Khairunnisa, Sarah, Nusyrotus Sa’dah, Isnani, Rohmah Artika, and Prihantini. "Forecasting and Effectiveness Analysis of Domestic Airplane Passengers in Yogyakarta Adisutjipto Airport with Autoregressive Integrated Moving Average with Exogeneous (ARIMAX) Model." Proceeding International Conference on Science and Engineering 3 (April 30, 2020): 365–69. http://dx.doi.org/10.14421/icse.v3.529.

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Airplane is one of the public transportations options that many people choose when traveling long distance. Nowadays, it is notes that the number of passengers domestic flight has increased from the previous months. This increase, especially occurs on the holidays, such as year-end holidays, Eid, and others. The increase of airplane passengers is inversely proportional to the number of available airplane. Forecasting the number of airplane passangers is necessary to prepare additional facilities when there is increasing passengers. This research focused on forecasting domestic airplane passengers at Adisucipto Airport, Yogyakarta using ARIMAX method to forecast the number of domestic airplane passengers and the effectiveness of domestic passengers at the international airport. The purpose of this research is to determine the best ARIMAX model and forecast airplane passengers in Adisucipto airport. The results will show the effectiveness of ARIMAX model with the effect of calendar variance on domestic airplane passenger forecasting at international airport. Based on the result of AIC and RMSE values, it shows that the ARIMAX(1,0,1) model with calendar variation is better than ARIMA(1,0,1) in predicting the number of airplane passengers at Yogyakarta Adisutjipto airport.

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Sasi, Archana, and Thiruselvan Subramanian. "Forecasting stochastic consumer portability visitation pattern in fair price shops of India." Journal of Information and Optimization Sciences 44, no. 3 (2023): 439–54. http://dx.doi.org/10.47974/jios-1364.

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In India, the Public Distribution System (PDS) is a critical tool for accomplishing the aim of “Zero Hunger”. Despite the enormous resources used, PDS has several inefficiencies that are caused by the monopoly of agents engaged in last-mile grain supply. Various state governments in India have been employing portability as an innovative solution to address this problem. In this article, we examined a huge-scale data on the deployment of portable beneficiaries arriving in a particular FPS of Kerala state in India over three years. A comparison is made between Auto-Regressive Integrated Moving Average (ARIMA) method which makes forecasts in univariate data and ARIMA with exogenous variables called ARIMAX. We followed Mean Absolute Percentage Error (MAPE) and Mean Absolute Deviation (MAD) as the accuracy performance measure of the models and observed that the ARIMAX model outperforms the ARIMA model with the least forecasting errors.

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Adekanmbi et al.,, Adekanmbi et al ,. "ARIMA and ARIMAX Stochastic Models for Fertility in Nigeria." International Journal of Mathematics and Computer Applications Research 7, no. 5 (2017): 1–20. http://dx.doi.org/10.24247/ijmcaroct20171.

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ALAM, WASI, MRINMOY RAY, RAJEEV RANJAN KUMAR, KANCHAN SINHA, SANTOSHA RATHOD, and K. N. SINGH. "Improved ARIMAX modal based on ANN and SVM approaches for forecasting rice yield using weather variables." Indian Journal of Agricultural Sciences 88, no. 12 (December 11, 2018): 1909–13. http://dx.doi.org/10.56093/ijas.v88i12.85446.

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An effort has been made to get precise forecast of rice yield through ARIMAX and proposed hybrid models using weather variables. In this article, two hybrid approaches like ARIMAX-ANN and ARIMAX-SVM have been proposed. Firstly, ARIMAX model was fitted for the considered time series data. Rice yield along with weather variables of Aligarh district of Uttar Pradesh have been considered to evaluate the forecasting performance of the proposed hybrid models. The residuals obtained from the fitted model which exhibit nonlinear pattern were fitted employing ANN and SVM. Using the fitted yield values through the hybrid approaches via ANN and SVM, MAPE under ARIMAX (0,1,1)-ANN and ARIMAX (0,1,1)-SVM are estimated to be 0.37 and 1.11, respectively, as compared to 12.18 under ARIMAX (0,1,1) model. Based on the results obtained, we infer that although performance of proposed ARIMAXSVM and ARIMAX-ANN models are close to each other but much superior to the conventional ARIMAX model for the considered data set. Performance of hybrid ARIMAX model is found to be quite encouraging. Yield has also been forecasted up to 2020 on the basis of forecasted rainfall using ARIMAX (0,1,1) model.

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Díaz Sosa, María Eliana, Edwin Andrés Cruz Pérez, and Wilmer Dario Pineda Ríos. "Modelamiento del precio de la papa criolla en el departamento de Cundinamarca por medio de series de tiempo y modelos dinámicos." Comunicaciones en Estadística 14, no. 1 (February 1, 2021): 31–52. http://dx.doi.org/10.15332/23393076.6633.

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El presente trabajo tiene como objetivo evaluar el comportamiento y pronóstico del precio de la papa criolla en el departamento de Cundinamarca, según los factores climáticos desde enero de 2012 hasta abril de 2018. Para ello, se tomaron en consideración, por un lado, análisis basados en series de tiempo (ARIMA, ARIMAX) y, por el otro, modelos lineales dinámicos (con y sin covariables). En los modelos trabajados se usaron como variables las condiciones climáticas de la zona en cuestión, a las cuales se les aplicó un método de imputación de datos debido a la ausencia de información. Luego fueron agrupados en tres factores construidos por Análisis Factorial para Series de Tiempo (TSFA). Finalmente, se procedió a comparar los indicadores de los cuatro modelos, llegando a la conclusión de que los modelos ARIMA Y ARIMAX generan las mejores predicciones respecto del precio de la papa criolla en el departamento de Cundinamarca.

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Titi, Dea Astri, Heri Kuswanto, and Suhartono Suhartono. "PERAMALAN LANGSUNG DAN TIDAK LANGSUNG MARKET SHARE MOBIL MENGGUNAKAN ARIMAX DENGAN EFEK VARIASI KALENDER." MEDIA STATISTIKA 13, no. 1 (June 19, 2020): 47–59. http://dx.doi.org/10.14710/medstat.13.1.47-59.

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Based on BPS data, the transportation industry sector contributed to about 8.01% of Indonesia's economic growth. The rapid growth of the transportation industry is also followed by the development of the automotive industry in Indonesia. The Exclusive Lisencee Agent of the Astra International group won a market share of 57% in April 2017. PT. Astra Daihatsu Motor, which is one of its subsidiaries, has a very rapid sales increase of 15% every year until Daihatsu's market share rises to 17.3%. Data from the Gabungan Industri Kendaraan Bermotor Indonesia (Gaikindo) shows an upward trend in car sales a month before Idul Fitri. This study carried out Daihatsu's direct and indirect market share forecasting using ARIMAX with a variety of calendar effects consisting of trends, monthly seasonal effects and Idul Fitri effects. The results indicated that indirect forecasting through forecasting the car sales for each brand and total market using ARIMAX outperforms the others and is able to capture the pattern of the testing data. The resulting SMAPE value of ARIMAX is smaller than direct forecasting and indirect forecasting using ARIMA.

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Musa, Mohammed Ibrahim. "Malaria Disease Distribution in Sudan Using Time Series ARIMA Model." International Journal of Public Health Science (IJPHS) 4, no. 1 (March 1, 2015): 7. http://dx.doi.org/10.11591/ijphs.v4i1.4705.

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Malaria is widely spread and distributed in the tropical and subtropical regions of the world. Sudan is a sub-Saharan African country that is highly affected by malaria with 7.5 million cases and 35,000 deaths every year. The auto-regressive integrated moving average (ARIMA) model was used to predict the spread of malaria in the Sudan. The ARIMA model used malaria cases from 2006 to 2011 as a training set, and data from 2012 as a testing set, and created the best model fitted to forecast the malaria cases in Sudan for years 2013 and 2014. The ARIMAX model was carried out to examine the relationship between malaria cases and climate factors with diagnostics of previous malaria cases using the least Bayesian Information Criteria (BIC) values. The results indicated that there were four different models, the ARIMA model of the average for the overall states is (1,0,1)(0,1,1)12. The ARIMAX model showed that there is a significant variation between the states in Sudan.

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Musa, Mohammed Ibrahim. "Malaria Disease Distribution in Sudan Using Time Series ARIMA Model." International Journal of Public Health Science (IJPHS) 4, no. 1 (March 1, 2015): 7. http://dx.doi.org/10.11591/.v4i1.4705.

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Malaria is widely spread and distributed in the tropical and subtropical regions of the world. Sudan is a sub-Saharan African country that is highly affected by malaria with 7.5 million cases and 35,000 deaths every year. The auto-regressive integrated moving average (ARIMA) model was used to predict the spread of malaria in the Sudan. The ARIMA model used malaria cases from 2006 to 2011 as a training set, and data from 2012 as a testing set, and created the best model fitted to forecast the malaria cases in Sudan for years 2013 and 2014. The ARIMAX model was carried out to examine the relationship between malaria cases and climate factors with diagnostics of previous malaria cases using the least Bayesian Information Criteria (BIC) values. The results indicated that there were four different models, the ARIMA model of the average for the overall states is (1,0,1)(0,1,1)12. The ARIMAX model showed that there is a significant variation between the states in Sudan.

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Qadrini, Laila, Asrirawan Asrirawan, Nur Mahmudah, Muhammad Fahmuddin, and Ihsan Fathoni Amri. "Forecasting Bank Indonesia Currency Inflow and Outflow Using ARIMA, Time Series Regression (TSR), ARIMAX, and NN Approaches in Lampung." Jurnal Matematika, Statistika dan Komputasi 17, no. 2 (December 23, 2020): 166–77. http://dx.doi.org/10.20956/jmsk.v17i2.11803.

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There are various types of data, one of which is the time-series data. This data type is capable of predicting future data with a similar speed as the forecasting method of analysis. This method is applied by Bank Indonesia (BI) in determining currency inflows and outflows in society. Moreover, Inflows and outflows of currency are monthly time-series data which are assumed to be influenced by time. In this study, several forecasting methods were used to predict this flow of currency including ARIMA, Time Series Regression (TSR), ARIMAX, and NN. Furthermore, RMSE accuracy was used in selecting the best method for predicting the currency flow. The results showed that the ARIMAX method was the best for forecasting because this method had the smallest RMSE.

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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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Silvia, Rara Hera, and Anneke Iswani Achmad. "Penerapan Metode ARIMAX dengan Efek Variasi Kalender pada Peramalan Harga Komoditas Cabai Rawit di Provinsi Jawa Barat." Bandung Conference Series: Statistics 3, no. 2 (August 2, 2023): 689–98. http://dx.doi.org/10.29313/bcss.v3i2.9180.

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Abstract. Forecasting is an important aid in planning and decision-making. One of the forecasting methods that is often used is the time series method. Time series are often influenced by a particular event or other variable, so it can cause the data to have a different repeating pattern each period. The ARIMAX Model is thought to be able to capture certain patterns by including certain event information as additional variables, or so-called exogenous variables. The tendency of data to show patterns at certain times based on dates in the calendar is called calendar variation, and the effect of calendar variation can appear on certain events such as Eid al-Fitr. Special treatment is needed for time series data with a calendar variation effect, where the ARIMAX model is well applied to the series data in that case, so that the model formed is ARIMAX with calendar variation. In this thesis, I conducted a study on forecasting the price of cayenne pepper in West Java province using the ARIMAX model with a variation of the calendar in which to include information on Eid al-Fitr events as an additional variable. The best ARIMAX model for forecasting is ARIMAX (0, 1, 1) with accuracy using a MAPE value of 11%, which is based on the criteria of forecasting ability. It can be concluded that the model ARIMAX (0, 1, 1) has good forecasting ability. Abstrak. Peramalan merupakan bantuan penting dalam perencanaan dan pengambilan keputusan. Salah satu metode peramalan yang sering digunakan adalah metode deret waktu (time series). Pada deret waktu seringkali dipengaruhi oleh suatu peristiwa tertentu atau variabel lain, sehingga dapat menyebabkan data memiliki pola berulang berbeda setiap periodenya. Model ARIMAX diduga mampu menangkap pola tertentu dengan memasukkan informasi peristiwa tertentu sebagai variabel tambahan atau disebut variabel eksogen. Adanya kecenderungan data untuk menampilkan pola pada waktu tertentu berdasarkan penanggalan di dalam kalender disebut variasi kalender, dimana efek variasi kalender dapat muncul pada peristiwa tertentu seperti Idul Fitri. Diperlukan perlakuan khusus untuk data deret waktu dengan efek variasi kalender, dimana model ARIMAX baik diterapkan untuk data deret dengan kasus tersebut, sehingga model yang terbentuk adalah ARIMAX dengan variasi kalender. Dalam skripsi ini melakukan penelitian pada peramalan harga cabai rawit di Provinsi Jawa Barat menggunakan model ARIMAX dengan variasi kalender dimana memasukkan informasi peristiwa Idul Fitri sebagai variabel tambahan. Didapatkan model ARIMAX terbaik untuk melakukan peramalan yaitu ARIMAX(0,1,1) dengan akurasi menggunakan nilai MAPE sebesar 11%, dimana berdasarkan kriteria kemampuan peramalan bahwa dapat disimpulkan model ARIMAX(0,1,1) memiliki kemampuan peramalan yang baik.

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T.O. Olatayo and K. I. Ekerikevwe. "Performance Measures for Evaluating the Accuracy of Time Series Hybrid Model Using High Frequency Data." Britain International of Exact Sciences (BIoEx) Journal 4, no. 3 (September 27, 2022): 244–59. http://dx.doi.org/10.33258/bioex.v4i3.760.

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Given that the traditional ARIMAX model has rarely been applied to any of the climate change and environmental agents, which are the most cognate agents with associated exogenous variables; to neutralize the model for a better and enhanced prediction of the system, a distributional form of the error term that is robust and sufficient in capturing and accommodating both the external covariate(s) and high frequency data is required. This study therefore evaluates the forecasting accuracy of two forecasting models namely ARIMAX and log-ARIMAX. The monthly adjusted high frequency data recorded by four Oil and Gas companies from 2005 – 2020 were used. The forecastability of the two models was evaluated with different error matrices. The effect of Akaike Information Criterion (AIC) and the linear correlation on candidate models among the considered oil spill data tested were discussed. Results for ARIMAX and LOG-ARIMAX Models selection with respect to AIC show that log-ARIMAX is more efficient and performed better than the traditional ARIMAX model for observations characterized by kurtosis, skewness, outliers, high frequency and large fluctuation series with heavy tailed traits as seen in environmental data.

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Wang, Yingdan, Chunjie Gao, Tiantian Zhao, Haiyan Jiao, Ying Liao, Zengyun Hu, and Lei Wang. "A comparative study of three models to analyze the impact of air pollutants on the number of pulmonary tuberculosis cases in Urumqi, Xinjiang." PLOS ONE 18, no. 1 (January 17, 2023): e0277314. http://dx.doi.org/10.1371/journal.pone.0277314.

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In this paper, we separately constructed ARIMA, ARIMAX, and RNN models to determine whether there exists an impact of the air pollutants (such as PM2.5, PM10, CO, O3, NO2, and SO2) on the number of pulmonary tuberculosis cases from January 2014 to December 2018 in Urumqi, Xinjiang. In addition, by using a new comprehensive evaluation index DISO to compare the performance of three models, it was demonstrated that ARIMAX (1,1,2) × (0,1,1)12 + PM2.5 (lag = 12) model was the optimal one, which was applied to predict the number of pulmonary tuberculosis cases in Urumqi from January 2019 to December 2019. The predicting results were in good agreement with the actual pulmonary tuberculosis cases and shown that pulmonary tuberculosis cases obviously declined, which indicated that the policies of environmental protection and universal health checkups in Urumqi have been very effective in recent years.

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Pektaş, Ali Osman, and H. Kerem Cigizoglu. "ANN hybrid model versus ARIMA and ARIMAX models of runoff coefficient." Journal of Hydrology 500 (September 2013): 21–36. http://dx.doi.org/10.1016/j.jhydrol.2013.07.020.

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Assakhiy, Rasyada, Samsul Anwar, and A. R. Fitriana. "PERAMALAN REALISASI PENERIMAAN ZAKAT PADA BAITULMAL ACEH DENGAN MEMPERTIMBANGKAN EFEK DARI VARIASI KALENDER." Jurnal Ekonomi Pembangunan 27, no. 2 (December 31, 2019): 27–45. http://dx.doi.org/10.14203/jep.27.2.2019.27-45.

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Baitulmal Aceh merupakan sebuah lembaga pemerintah daerah Provinsi Aceh yang bertanggung jawab sebagai pengelola dan pendistribusi zakat, infak dan sedekah (ZIS). Peramalan potensi zakat pada masa yang akan datang dibutuhkan oleh Baitulmal Aceh sebagai salah satu landasan penyusunan kebijakan pengelolaan ZIS. Penelitian ini bertujuan untuk meramalkan potensi zakat yang terkumpul pada tahun 2018 dan 2019 dengan mempertimbangkan efek dari variasi kalender. Data yang digunakan dalam penelitian ini adalah data realisasi penerimaan zakat bulanan mulai dari bulan Januari 2015 hingga Desember 2017 yang diperoleh dari Baitulmal Aceh. Data tersebut dianalisis dengan model Autoregressive Integrated Moving Average with Exogenous Variable (ARIMAX) dan Seasonal Autoregressive Integrated Moving Average (SARIMA) sebagai model pembanding. Hasil penelitian menunjukkan bahwa model ARIMAX dengan orde ARIMA(2,0,2) (1,0,2)12, t, V1, ..., V11 jauh lebih baik daripada model SARIMA dengan orde ARIMA(0,1,2)(0,1,1)12 berdasarkan indikator ketepatan hasil ramalannya (RMSE dan MAPE). Realisasi penerimaan zakat pada tahun 2018 dan 2019 masing-masing diperkirakan sebesar Rp. 1.347.526.504 dan Rp. 1.359.728.268. Hasil peramalan tersebut dapat digunakan sebagai salah satu rujukan bagi Baitulmal Aceh dalam menyusun kebijakan pendistribusian zakat pada tahun-tahun yang akan datang.

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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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Obi, C. V., and C. N. Okoli. "Comparative Performance of the ARIMA, ARIMAX and SES Model for Estimating Reported Cases of Diabetes Mellitus in Anambra State, Nigeria." European Journal of Engineering and Technology Research 6, no. 1 (January 12, 2021): 63–68. http://dx.doi.org/10.24018/ejers.2021.6.1.2321.

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This study examined the performance of the ARIMA, ARIMAX and the Single Exponential Smoothing (SES) model for the estimation of diabetes cases in Anambra State with the following specific objectives: to fit the model to the data, to determine the best fit model for estimating diabetes mellitus cases and forecast for expected cases for period of five years. The secondary data used for the study is sourced from records of Anambra state Ministry of Health. The Akaike information criterion is adopted for assessing the performance of the models. The R-software is employed for the analysis of data. The results obtained showed that the data satisfied normality and stationarity requirements. The finding of the study showed that ARIMA model has least value of AIC of 1177.92, following the ARIMAX model with value of AIC=1542.25 and SEM recorded highest value of 1595.67. The findings further revealed that the ARIMA has the least values across the measures of accuracy. More so, five years predictions of the cases of diabetes mellitus were obtained using the models under study. From the results of the findings, ARIMA model proved to be best alternative for estimating reported cases of diabetes mellitus in Anambra state. Based on the findings, we recommend there is need for medical practitioners /health planners to create awareness and inform patients about the possible related risk factors of death through early diagnosis and intervention.

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Obi, C. V., and C. N. Okoli. "Comparative Performance of the ARIMA, ARIMAX and SES Model for Estimating Reported Cases of Diabetes Mellitus in Anambra State, Nigeria." European Journal of Engineering and Technology Research 6, no. 1 (January 12, 2021): 63–68. http://dx.doi.org/10.24018/ejeng.2021.6.1.2321.

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This study examined the performance of the ARIMA, ARIMAX and the Single Exponential Smoothing (SES) model for the estimation of diabetes cases in Anambra State with the following specific objectives: to fit the model to the data, to determine the best fit model for estimating diabetes mellitus cases and forecast for expected cases for period of five years. The secondary data used for the study is sourced from records of Anambra state Ministry of Health. The Akaike information criterion is adopted for assessing the performance of the models. The R-software is employed for the analysis of data. The results obtained showed that the data satisfied normality and stationarity requirements. The finding of the study showed that ARIMA model has least value of AIC of 1177.92, following the ARIMAX model with value of AIC=1542.25 and SEM recorded highest value of 1595.67. The findings further revealed that the ARIMA has the least values across the measures of accuracy. More so, five years predictions of the cases of diabetes mellitus were obtained using the models under study. From the results of the findings, ARIMA model proved to be best alternative for estimating reported cases of diabetes mellitus in Anambra state. Based on the findings, we recommend there is need for medical practitioners /health planners to create awareness and inform patients about the possible related risk factors of death through early diagnosis and intervention.

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YÜCESAN, MELİH. "YSA, ARIMA ve ARIMAX Yöntemleriyle Satış Tahmini: Beyaz Eşya Sektöründe bir Uygulama - Sales Forecast with YSA, ARIMA and ARIMAX Methods: An Application in the White Goods Sector." Journal of Business Research - Turk 10, no. 1 (March 30, 2018): 689–706. http://dx.doi.org/10.20491/isarder.2018.414.

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Intan, Solikhah Novita, Etik Zukhronah, and Supriyadi Wibowo. "Peramalan Banyaknya Pengunjung Pantai Glagah Menggunakan Metode Autoregressive Integrated Moving Average Exogenous (ARIMAX) dengan Efek Variasi Kalender." Indonesian Journal of Applied Statistics 1, no. 2 (March 13, 2019): 70. http://dx.doi.org/10.13057/ijas.v1i2.26298.

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<pre>Glagah Beach is one of the tourist destinations in Kulon Progo Regency, Yogyakarta which is the most visited by tourists. Glagah Beach visitors data show that in the month of Eid Al-Fitr there was a significant increase. This shows that there is an effect of the calendar variation of Eid al-Fitr. Therefore, it is needed a method that can be used to analyze time series data which contains effects of calendar variations, that is ARIMAX method. The aim of this study are to find the best ARIMAX model and to predict the number of visitors to Glagah Beach in the future. The result shows that the best ARIMAX model was ARIMAX([24],0,0). Forecasting from January to September 2016 are 37211, 21306, 26247, 24148, 28402, 29309, 81724, 26029, and 23688 visitors.</pre> Keywords: Glagah Beach; variation of calendar; Eid al-Fitr; ARIMAX.

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Susila, Muktar Redy. "PENGARUH HARI RAYA IDUL FITRI TERHADAP INFLASI DI INDONESIA DENGAN PENDEKATAN ARIMAX (VARIASI KALENDER)." BAREKENG: Jurnal Ilmu Matematika dan Terapan 14, no. 3 (October 10, 2020): 369–78. http://dx.doi.org/10.30598/barekengvol14iss3pp369-378.

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Abstrak Tujuan dari penilitian ini yaitu meneliti pengaruh dari hari raya Idul Fitri terhadap inflasi bulanan di Indonesia. Digunakan metode ARIMAX (Variasi Kalender) untuk mengetahui besar pengaruh dari Idul Fitri terhadap inflasi bulanan di Indonesia. Karakteristik inflasi Juli 2008 hingga Juni 2019 memiliki keunikan. Rata-rata inflasi bulanan yaitu 0,39 dan varians inflasi bulanan yaitu 0,26. Berdasarkan model ARIMAX menunjukan bahwa bulan Januari, Mei, Juni, Juli, Agustus, November, Desember, dan hari raya Idul Fitri memberikan pengaruh signifikan terhadap inflasi bulanan Indonesia. Efek yang diberikan hari raya Idhul Fitri yaitu sebesar 0,47. Pada saat bulan Idul Fitri tiba angka inflasinya akan lebih tinggi sebesar 0,47 dibandingkan bulan lainnya. Kata Kunci : Inflasi, ARIMAX, Idul Fitri. Abstract The purpose of this study is to calculate the effect of Eid Al-Fitr to Indonesian monthly inflation. The ARIMAX (Calendar Variation) method is used to determine the effect of Eid Al-Fitr on Indonesian monthly inflation. The characteristics of inflation in July 2008 to June 2019 are unique. The average of inflation is 0,39 and the variance of inflation is 0,26. The ARIMAX model shows that January, May, June, July, August, November, December, and Eid Al-Fitr has a significant influence on Indonesian monthly inflation. The effect of the Eid Al-Fitr was 0,47. When the Eid Al-Fitr arrives, the inflation rate will be higher 0,47 than other months. Keywords: Inflation, ARIMAX, Eid al-Fitr.

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Putri, J. A., Suhartono Suhartono, H. Prabowo, N. A. Salehah, D. D. Prastyo, and Setiawan Setiawan. "Forecasting Currency in East Java: Classical Time Series vs. Machine Learning." Indonesian Journal of Statistics and Its Applications 5, no. 2 (June 30, 2021): 284–303. http://dx.doi.org/10.29244/ijsa.v5i2p284-303.

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Most research about the inflow and outflow currency in Indonesia showed that these data contained both linear and nonlinear patterns with calendar variation effect. The goal of this research is to propose a hybrid model by combining ARIMAX and Deep Neural Network (DNN), known as hybrid ARIMAX-DNN, for improving the forecast accuracy in the currency prediction in East Java, Indonesia. ARIMAX is class of classical time series models that could accurately handle linear pattern and calendar variation effect. Whereas, DNN is known as a machine learning method that powerful to tackle a nonlinear pattern. Data about 32 denominations of inflow and outflow currency in East Java are used as case studies. The best model was selected based on the smallest value of RMSE and sMAPE at the testing dataset. The results showed that the hybrid ARIMAX-DNN model improved the forecast accuracy and outperformed the individual models, both ARIMAX and DNN, at 26 denominations of inflow and outflow currency. Hence, it can be concluded that hybrid classical time series and machine learning methods tend to yield more accurate forecasts than individual models, both classical time series and machine learning methods.

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Barzola-Monteses, Julio, Mónica Mite-León, Mayken Espinoza-Andaluz, Juan Gómez-Romero, and Waldo Fajardo. "Time Series Analysis for Predicting Hydroelectric Power Production: The Ecuador Case." Sustainability 11, no. 23 (November 20, 2019): 6539. http://dx.doi.org/10.3390/su11236539.

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Electrical generation in Ecuador mainly comes from hydroelectric and thermo-fossil sources, with the former amounting to almost half of the national production. Even though hydroelectric power sources are highly stable, there is a threat of droughts and floods affecting Ecuadorian water reservoirs and producing electrical faults, as highlighted by the 2009 Ecuador electricity crisis. Therefore, predicting the behavior of the hydroelectric system is crucial to develop appropriate planning strategies and a good starting point for energy policy decisions. In this paper, we developed a time series predictive model of hydroelectric power production in Ecuador. To this aim, we used production and precipitation data from 2000 to 2015 and compared the Box-Jenkins (ARIMA) and the Box-Tiao (ARIMAX) regression methods. The results showed that the best model is the ARIMAX (1,1,1) (1,0,0)12, which considers an exogenous variable precipitation in the Napo River basin and can accurately predict monthly production values up to a year in advance. This model can provide valuable insights to Ecuadorian energy managers and policymakers.

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Munir, Said, and Martin Mayfield. "Application of Density Plots and Time Series Modelling to the Analysis of Nitrogen Dioxides Measured by Low-Cost and Reference Sensors in Urban Areas." Nitrogen 2, no. 2 (April 13, 2021): 167–95. http://dx.doi.org/10.3390/nitrogen2020012.

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Temporal variability of NO2 concentrations measured by 28 Envirowatch E-MOTEs, 13 AQMesh pods, and eight reference sensors (five run by Sheffield City Council and three run by the Department for Environment, Food and Rural Affairs (DEFRA)) was analysed at different time scales (e.g., annual, weekly and diurnal cycles). Density plots and time variation plots were used to compare the distributions and temporal variability of NO2 concentrations. Long-term trends, both adjusted and non-adjusted, showed significant reductions in NO2 concentrations. At the Tinsley site, the non-adjusted trend was −0.94 (−1.12, −0.78) µgm−3/year, whereas the adjusted trend was −0.95 (−1.04, −0.86) µgm−3/year. At Devonshire Green, the non-adjusted trend was −1.21 (−1.91, −0.41) µgm−3/year and the adjusted trend was −1.26 (−1.57, −0.83) µgm−3/year. Furthermore, NO2 concentrations were analysed employing univariate linear and nonlinear time series models and their performance was compared with a more advanced time series model using two exogenous variables (NO and O3). For this purpose, time series data of NO, O3 and NO2 were obtained from a reference site in Sheffield, which were more accurate than the measurements from low-cost sensors and, therefore, more suitable for training and testing the model. In this article, the three main steps used for model development are discussed: (i) model specification for choosing appropriate values for p, d and q, (ii) model fitting (parameters estimation), and (iii) model diagnostic (testing the goodness of fit). The linear auto-regressive integrated moving average (ARIMA) performed better than the nonlinear counterpart; however, its performance in predicting NO2 concentration was inferior to ARIMA with exogenous variables (ARIMAX). Using cross-validation ARIMAX demonstrated strong association with the measured concentrations, with a correlation coefficient of 0.84 and RMSE of 9.90. ARIMAX can be used as an early warning tool for predicting potential pollution episodes in order to be proactive in adopting precautionary measures.

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Nourani, Vahid, Samira Roumianfar, and Elnaz Sharghi. "Using Hybrid ARIMAX-ANN Model for Simulating Rainfall - Runoff - Sediment Process Case Study." International Journal of Applied Metaheuristic Computing 4, no. 2 (April 2013): 44–60. http://dx.doi.org/10.4018/jamc.2013040104.

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The need for accurate modeling of rainfall-runoff-sediment processes has grown rapidly in the past decades. This study investigates the efficiency of black-box models including Artificial Neural Network (ANN) and Autoregressive Integrated Moving Average with eXogenous input (ARIMAX) models for forecasting the rainfall-runoff-sediment process. According to the complex behavior of the rainfall-runoff-sediment time series, they include both linear and nonlinear components; therefore, employing a hybrid model which combines the advantages of both linear and non-linear models improves the accuracy of prediction. In this paper, a hybrid of ARIMAX-ANN model is applied to rainfall-runoff-sediment modeling of a watershed. At the first step of the hybrid modeling, the ARIMAX method is applied to forecast the linear component of the rainfall-runoff process and then in the second step, an ANN model is used to find the non-linear relationship among the residuals of the fitted linear ARIMAX model. Finally, total effective time series of runoff, obtained by the hybrid ARIMAX-ANN model are imposed as input to the proposed ANN model for prediction daily suspended sediment load of the watershed. The proposed model is more appropriate, as it uses the semi-linear relation for prediction of sediment load.

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Agbenyega, Diana Ayorkor, John Andoh, Samuel Iddi, and Louis Asiedu. "Modelling Customs Revenue in Ghana Using Novel Time Series Methods." Applied Computational Intelligence and Soft Computing 2022 (April 18, 2022): 1–8. http://dx.doi.org/10.1155/2022/2111587.

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Governments across the world rely on their Customs Administration to provide functions that include border security, intellectual property rights protection, environmental protection, and revenue mobilisation amongst others. Analyzing the trends in revenue being collected from Customs is necessary to direct government policies and decisions. Models that can capture the trends being purported from the nominal (nonreal) tax values with respect to the trade volumes (value) over the period are indispensable. Predominant amongst the existing models are the econometric models (the GDP-based model, the monthly receipts model, and the microsimulation model), which are laborious and sometimes unreliable when studying trends in time series data. In this study, we modelled monthly revenue data obtained from the Ghana Revenue Authority-Customs Division (GRA-CD) for the period January 2010 to December 2019 using two traditional time series models, ARIMA model and ARIMA Error Regression Model (ARIMAX), and two machine learning time series models, Bayesian Structural Time Series (BSTS) model and a Neural Network Autoregression model. The Neural Network Autoregression model of the form NNAR (1, 3) provided the best forecasts with the least Mean Squared Error (MSE) of 53.87 and relatively lower Mean Absolute Percentage Error (MAPE) of 0.08. Generally, the machine learning models (NNAR (1, 3) and BSTS) outperformed the traditional time series models (ARIMA and ARIMAX models). The forecast values from the NNAR (1, 3) indicated a potential decline in revenue and this emphasizes the need for relevant authorities to institute measures to improve revenue generation in the immediate future.

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Lee, Hang-Lo, Ki-Il Song, Chongchong Qi, Jin-Seop Kim, and Kyoung-Su Kim. "Real-Time Prediction of Operating Parameter of TBM during Tunneling." Applied Sciences 11, no. 7 (March 26, 2021): 2967. http://dx.doi.org/10.3390/app11072967.

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With the increasing use of the tunnel boring machine (TBM), attempts have been made to predict TBM operating parameters. Prediction of operating parameters is still an important step in the adaptability of the TBM for the future. In this study, we employ a walk forward (WF) prediction method based on ARIMAX, which can consider time-varying features and geological conditions. This method is applied to two different TBM projects to evaluate its performance, and is then compared with WF based on ordinary least squares (OLS). The simulation results show that the ARIMAX predictor outperforms the OLS predictor in both projects. For practical applications, an additional analysis is carried out according to the real-time prediction distance. The results show that time series-based ARIMAX provides meaningful results in 8 rings (11 m) or less of real-time prediction distance. The WF based on ARIMAX can provide reasonable TBM operating conditions with time-varying data and can be utilized in decision-making to improve excavation performance.

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Riestiansyah, Faiq, Devina Damayanti, Miranda Reswara, and Ronny Susetyoko. "Perbandingan metode ARIMA dan ARIMAX dalam Memprediksi Jumlah Wisatawan Nusantara di Pulau Bali." Jurnal Infomedia 7, no. 2 (December 9, 2022): 58. http://dx.doi.org/10.30811/jim.v7i2.3336.

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Indonesia memiliki berbagai potensi pemanfaatan yang berbeda tergantung dari sumber daya alamnya seperti bahan tambang, lahan pertanian, pariwisata dan lain-lain. Untuk meningkatkan pendapatan pada sektor pariwisata diperlukan data peramalan jumlah wisatawan yang berkunjung ke Pulau Bali. Data hasil peramalan tersebut dapat menjadi acuan untuk pengembangan dan pengoptimalisasian hal yang perlu diperbaiki di sektor kepariwisataan ini. Tujuan dari dilakukannya penelitian ini adalah untuk mengetahui perbandingan hasil prediksi terhadap Jumlah Wisatawan Nusantara yang berkunjung ke Pulau Bali. Salah satu model yang sering digunakan untuk masalah peramalan adalah model ARIMA. Model ARIMA yang juga disebut Runtut Waktu Box-Jenkins ini hanya cocok digunakan untuk kasus peramalan jangka pendek, karena jika digunakan untuk peramalan jangka panjang, model ini biasanya akan cenderung menghasilkan grafik time series datar. Setelah melakukan kedua pemodelan (ARIMA dan ARIMAX) selanjutnya membandingkan performa kedua model tersebut dalam melakukan prediksi Jumlah Wisatawan Nusantara yang berkunjung ke Pulau Bali dalam waktu tertentu dengan melihat error (RMSE) dari masing - masing model. Semakin rendah nilai RMSE maka semakin baik model tersebut bekerja dalam melakukan prediksi. Harapannya hasil dari penelitian ini dapat dimanfaatkan oleh siapapun yang memiliki kepentingan dalam pengembangan sektor pariwisata di Pulau Bali.

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Yang, Sheng-xiong, Hong-feng Xu, Yong-jia Mao, Zu-hua Liang, and Chun-liu Pan. "Predicting the Number of Reported Pulmonary Tuberculosis in Guiyang, China, Based on Time Series Analysis Techniques." Computational and Mathematical Methods in Medicine 2022 (October 30, 2022): 1–14. http://dx.doi.org/10.1155/2022/7828131.

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Tuberculosis (TB) is one of the world’s deadliest infectious disease killers today, and despite China’s increasing efforts to prevent and control TB, the TB epidemic is still very serious. In the context of the COVID-19 pandemic, if reliable forecasts of TB epidemic trends can be made, they can help policymakers with early warning and contribute to the prevention and control of TB. In this study, we collected monthly reports of pulmonary tuberculosis (PTB) in Guiyang, China, from January 1, 2010 to December 31, 2020, and monthly meteorological data for the same period, and used LASSO regression to screen four meteorological factors that had an influence on the monthly reports of PTB in Guiyang, including sunshine hours, relative humidity, average atmospheric pressure, and annual highest temperature, of which relative humidity (6-month lag) and average atmospheric pressure (7-month lag) have a lagging effect with the number of TB reports in Guiyang. Based on these data, we constructed ARIMA, Holt-Winters (additive and multiplicative), ARIMAX (with meteorological factors), LSTM, and multivariable LSTM (with meteorological factors). We found that the addition of meteorological factors significantly improved the performance of the time series prediction model, which, after comprehensive consideration, included the ARIMAX (1,1,1) (0,1,2)12 model with a lag of 7 months at the average atmospheric pressure, outperforms the other models in terms of both fit ( RMSE = 37.570 , MAPE = 10.164 % , MAE = 28.511 ) and forecast sensitivity ( RMSE = 20.724 , MAPE = 6.901 % , MAE = 17.306 ), so the ARIMAX (1,1,1) (0,1,2)12 model with a lag of 7 months can be used as a predictor tool for predicting the number of monthly reports of PTB in Guiyang, China.

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Syam, Andy Rezky Pratama. "Application of the Autoregressive Integrated Moving Average Exogenous (ARIMAX) with Calendar Variation Effect Method for Forecasting Chocolate Data in Indonesia and the United States." Jurnal Matematika, Statistika dan Komputasi 18, no. 2 (January 1, 2022): 224–36. http://dx.doi.org/10.20956/j.v18i2.18460.

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Forecasting chocolate consumption is required by producers in preparing the amount of production each month. The tradition of Valentine, Christmas and Eid al-Fitr which are closely related to chocolate makes it impossible to predict chocolate by using the Classical Time Series method. Especially for Eid al-Fitr, the determination follows the Hijri calendar and each year advances 10 days on the Masehi calendar, so that every three years Eid al-Fitr will occur in a different month. Based on this, the chocolate forecasting will show a variation calendar effect. The method used in modeling and forecasting chocolate in Indonesia and the United States is the ARIMAX (Autoregressive Integrated Moving Average Exogenous) method with Calendar Variation effect. As a comparison, modeling and forecasting are also carried out using the Naïve Trend Linear, Naïve Trend Exponential, Double Exponential Smoothing, Time Series Regression, and ARIMA methods. The ARIMAX method with Calendar Variation Effect produces a very precise MAPE value in predicting chocolate data in Indonesia and the United States. The resulting MAPE value is below 10 percent, so it can be concluded that this method has a very good ability in forecasting.

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Yucesan, Melih, Muhammet Gul, and Erkan Celik. "Performance Comparison between ARIMAX, ANN and ARIMAX-ANN Hybridization in Sales Forecasting for Furniture Industry." Drvna industrija 69, no. 4 (2018): 357–70. http://dx.doi.org/10.5552/drind.2018.1770.

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Du, Zhicheng, Lin Xu, Wangjian Zhang, Dingmei Zhang, Shicheng Yu, and Yuantao Hao. "Predicting the hand, foot, and mouth disease incidence using search engine query data and climate variables: an ecological study in Guangdong, China." BMJ Open 7, no. 10 (October 2017): e016263. http://dx.doi.org/10.1136/bmjopen-2017-016263.

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ObjectivesHand, foot, and mouth disease (HFMD) has caused a substantial burden in China, especially in Guangdong Province. Based on the enhanced surveillance system, we aimed to explore whether the addition of temperate and search engine query data improves the risk prediction of HFMD.DesignEcological study.Setting and participantsInformation on the confirmed cases of HFMD, climate parameters and search engine query logs was collected. A total of 1.36 million HFMD cases were identified from the surveillance system during 2011–2014. Analyses were conducted at aggregate level and no confidential information was involved.Outcome measuresA seasonal autoregressive integrated moving average (ARIMA) model with external variables (ARIMAX) was used to predict the HFMD incidence from 2011 to 2014, taking into account temperature and search engine query data (Baidu Index, BDI). Statistics of goodness-of-fit and precision of prediction were used to compare models (1) based on surveillance data only, and with the addition of (2) temperature, (3) BDI, and (4) both temperature and BDI.ResultsA high correlation between HFMD incidence and BDI (r=0.794, p<0.001) or temperature (r=0.657, p<0.001) was observed using both time series plot and correlation matrix. A linear effect of BDI (without lag) and non-linear effect of temperature (1 week lag) on HFMD incidence were found in a distributed lag non-linear model. Compared with the model based on surveillance data only, the ARIMAX model including BDI reached the best goodness-of-fit with an Akaike information criterion (AIC) value of −345.332, whereas the model including both BDI and temperature had the most accurate prediction in terms of the mean absolute percentage error (MAPE) of 101.745%.ConclusionsAn ARIMAX model incorporating search engine query data significantly improved the prediction of HFMD. Further studies are warranted to examine whether including search engine query data also improves the prediction of other infectious diseases in other settings.

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Islam, Farhana, and Monzur Alam Imteaz. "Use of Teleconnections to Predict Western Australian Seasonal Rainfall Using ARIMAX Model." Hydrology 7, no. 3 (August 5, 2020): 52. http://dx.doi.org/10.3390/hydrology7030052.

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Increased demand for engineering propositions to forecast rainfall events in an area or region has resulted in developing different rainfall prediction models. Interestingly, rainfall is a very complicated natural system that requires consideration of various attributes. However, regardless of the predictability performance, easy to use models have always been welcomed over the complex and ambiguous alternatives. This study presents the development of Auto–Regressive Integrated Moving Average models with exogenous input (ARIMAX) to forecast autumn rainfall in the South West Division (SWD) of Western Australia (WA). Climate drivers such as Indian Ocean Dipole (IOD) and El Nino Southern Oscillation (ENSO) were used as predictors. Eight rainfall stations with 100 years of continuous data from two coastal regions (south coast and north coast) were selected. In the south coast region, Albany (0,1,1) with exogenous input DMIOct–Nino3Nov, and Northampton (0,1,1) with exogenous input DMIJan–Nino3Nov were able to forecast autumn rainfall 4 months and 2 months in advance, respectively. Statistical performance of the ARIMAX model was compared with the multiple linear regression (MLR) model, where for calibration and validation periods, the ARIMAX model showed significantly higher correlations (0.60 and 0.80, respectively), compared to the MLR model (0.44 and 0.49, respectively). It was evident that the ARIMAX model can predict rainfall up to 4 months in advance, while the MLR has shown strict limitation of prediction up to 1 month in advance. For WA, the developed ARIMAX model can help to overcome the difficulty in seasonal rainfall prediction as well as its application can make an invaluable contribution to stakeholders’ economic preparedness plans.

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Pradewita, Wella Cintya, Nur Karomah Dwidayati, and Sugiman Sugiman. "Peramalan Volatilitas Risiko Berinvestasi Saham Menggunakan Metode GARCH–M dan ARIMAX–GARCH." Indonesian Journal of Mathematics and Natural Sciences 44, no. 1 (April 12, 2021): 12–21. http://dx.doi.org/10.15294/ijmns.v44i1.32701.

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Model GARCH–M merupakan pengembangan model GARCH yang dimasukkan variansi bersyarat ke dalam persamaan mean. Model ARIMAX–GARCH merupakan penggabungan model ARIMAX dan GARCH. Kedua model tersebut dapat digunakan untuk mengatasi masalah heteroskedastisitas pada data. Penelitian ini bertujuan menemukan model terbaik untuk peramalan volatilitas risiko berinvestasi saham. Penelitian ini menggunakan literature dengan tahapan perumusan masalah, pengumpulan data, pengolahan dan analisis data, serta penarikan kesimpulan. Dalam analisis dan pembahasan meliputi statistika deskriptif, uji stasioneritas, pembentukan dan menentukan model terbaik kedua model, pembandingan kedua model, dan peramalan volatilitas saham. Dari hasil penelitian ini diperoleh model terbaik untuk peramalan volatilitas saham yaitu GARCH (1,1) – M dengan nilai MAPE=118,0299 lebih kecil dibanding nilai MAPE pada model ARIMAX (2,1,2)– GARCH (1,1) =191,3115. Berdasarkan model terbaik tersebut diperoleh hasil peramalan volatilitas saham sebesar 0,07629 dan apabila dana yang dialokasikan oleh investor saham sebesar Rp 200.000.000, 00 maka nilai VaR yang diperoleh sebesar Rp 85.615.826,00.GARCH-M is an expansion of the GARCH model that entered conditional variance into the mean equation. ARIMAX - GARCH is combination of ARIMAX model and GARCH model. Both models can be used to solve the problem of heteroscedasticity on data. The purpose of this research was to find the best model for forecasting of the risk of investing in stocks. The method of this research was problem formulation, data collection, data processing and analysis, and conclusions. In the analysis and discussion include descriptive statistics, stationary test, estimate and determine the best models of both models, comparison of both models, and stock volatility forecasting. The results of this research obtained the best model for forecasting of stock volatility is GARCH (1,1) - M with MAPE value = 118.0299 smaller than MAPE value of ARIMAX (2,1,2) - GARCH (1,1) = 191, 3115. Based on the best model is obtained forecasting of stock volatility is 0.07629 and if the fund allocated by investors are Rp 200,000,000.00, so the value of VaR obtained Rp 85.615.826,00.

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Jadevicius, Arvydas, and Simon Huston. "Property market modelling and forecasting: simple vs complex models." Journal of Property Investment & Finance 33, no. 4 (July 6, 2015): 337–61. http://dx.doi.org/10.1108/jpif-08-2014-0053.

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Purpose – The commercial property market is complex, but the literature suggests that simple models can forecast it. To confirm the claim, the purpose of this paper is to assess a set of models to forecast UK commercial property market. Design/methodology/approach – The employs five modelling techniques, including Autoregressive Integrated Moving Average (ARIMA), ARIMA with a vector of an explanatory variable(s) (ARIMAX), Simple Regression (SR), Multiple Regression, and Vector Autoregression (VAR) to model IPD UK All Property Rents Index. The Bank Rate, Construction Orders, Employment, Expenditure, FTSE AS Index, Gross Domestic Product (GDP), and Inflation are all explanatory variables selected for the research. Findings – The modelling results confirm that increased model complexity does not necessarily yield greater forecasting accuracy. The analysis shows that although the more complex VAR specification is amongst the best fitting models, its accuracy in producing out-of-sample forecasts is poorer than of some less complex specifications. The average Theil’s U-value for VAR model is around 0.65, which is higher than that of less complex SR with Expenditure (0.176) or ARIMAX (3,0,3) with GDP (0.31) as an explanatory variable models. Practical implications – The paper calls analysts to make forecasts more user-friendly, which are easy to use or understand, and for researchers to pay greater attention to the development and improvement of simpler forecasting techniques or simplification of more complex structures. Originality/value – The paper addresses the issue of complexity in modelling commercial property market. It advocates for simplicity in modelling and forecasting.

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Maghfiroh, Z. F., Suhartono, H. Prabowo, N. A. Salehah, D. D. Prastyo, and Setiawan. "Forecasting Inflow and Outflow of Currency in Central Java using ARIMAX, RBFN and Hybrid ARIMAX-RBFN." Journal of Physics: Conference Series 1863, no. 1 (March 1, 2021): 012066. http://dx.doi.org/10.1088/1742-6596/1863/1/012066.

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Elvina Catria, Atus Amadi Putra, Dony Permana, and Dina Fitria. "Adding Exogenous Variable in Forming ARIMAX Model to Predict Export Load Goods in Tanjung Priok Port." UNP Journal of Statistics and Data Science 1, no. 1 (February 9, 2023): 31–38. http://dx.doi.org/10.24036/ujsds/vol1-iss1/10.

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The main idea of world maritime has been launched by the Indonesia’s Government through the development of inter-island connectivity, namely a logistics distribution line system using cargo ships with scheduled routes. However, in terms of inter-island sea transportation connectivity using sea transportation, the number of ships used for loading and unloading activities at Tanjung Priok in 2020 reached 11,876 units, which number decreased by 12.6% compared to the previous year, this figure was not sufficient for transportation of Indonesian loading and unloading goods (exports). This condition is important to note because the implementation of sea transportation, especially for sea toll transportation, if it cannot reach all regions, will cause freight transportation in some areas to be limited and regional economic growth cannot be distributed evenly. The purpose of this study is to predict the number of goods loaded (exported) at the Port of Tanjung Priok, by establishing an export forecasting model. Exogenous variable in the form of the Indonesian Wholesale Price Index. After analyzing the data, the order of the ARIMA model (5,1,1) was obtained as a parameter to estimate the ARIMAX model. From the ARIMAX model (5,1,1), the model's accuracy rate is 13.25% which is quite feasible to use to predict the total export cargo for the period January 2021-December 2021. Forecasting results show better fluctuations than in 2020.

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Journal articles on the topic 'ARIMAX'

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