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Journal articles on the topic 'ARIMA models'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Mahmudi, Mahmudi, Afnenda Rachmalia Hidayat, and Madona Yunita Wijaya. "The Effect of Ensemble Averaging Method on Rainfall Forecasting in Jakarta Using ARIMA and ARIMAX." Mathline : Jurnal Matematika dan Pendidikan Matematika 9, no. 2 (2024): 501–12. http://dx.doi.org/10.31943/mathline.v9i2.608.

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This research discusses rainfall modeling using ARIMA and ARIMAX models in Jakarta. This is important because rainfall forecasting in Jakarta has a significant impact on flooding and infrastructure. The focus of this research is on significant ARIMA and ARIMAX models, which are then subtotaled using ensemble averaging. Humidity and temperature variables are of particular interest in ARIMAX modeling due to their high correlation with rainfall. This quantitative research uses secondary data analysis from Tanjung Priok and Kemayoran Stations through the BMKG website, from July 2018 to June 2023.

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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 (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 forecas

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Hedi, Ahmad Deni Mulyadi, Sapto Prajogo, and Agus Binarto. "Predicting Waste Volume Using ARIMA and ARFIMA Models." International Research Journal of Multidisciplinary Scope 06, no. 02 (2025): 97–105. https://doi.org/10.47857/irjms.2025.v06i02.03143.

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The final waste processing facility plays a crucial role in waste management. The growing amount of waste in landfills is causing significant harm to the surrounding environment and the health of nearby residents. This study seeks to offer insights into the projected future waste volume in landfills. This research applies the mathematical models of the Autoregressive Integrated Moving Average (ARIMA) and Autoregressive Fractionally Integrated Moving Average (ARFIMA). This research method begins by determining the source of monthly waste data at the final waste disposal place. Based on monthly

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Pokhrel, Aditya, and Renisha Adhikari. "Leveraging Exogenous Insights: A Comparative Forecast of Paddy Production in Nepal Using ARIMA and ARIMAX Models." Economic Review of Nepal 6, no. 1 (2023): 52–69. http://dx.doi.org/10.3126/ern.v6i1.67970.

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Using annual time series data from 1975 to 2022, this study analyzed the ARIMA (1,1,7) and ARIMAX (1,1,7) models to improve paddy production forecasts in Nepal. The ARIMA model was initially employed to forecast paddy production. The availability of agricultural land was subsequently included as an exogenous variable in the ARIMAX model (after a significant endogeneity test) to increase precision. In contrast to the ARIMA model, which predicted paddy production of 5787.64 metric tons per hectare for the year 2022, the ARIMAX model predicted 5681.17 metric tons per hectare. Compared to the ARIM

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Luo, Tingyan, Jie Zhou, Jing Yang, et al. "Early Warning and Prediction of Scarlet Fever in China Using the Baidu Search Index and Autoregressive Integrated Moving Average With Explanatory Variable (ARIMAX) Model: Time Series Analysis." Journal of Medical Internet Research 25 (October 30, 2023): e49400. http://dx.doi.org/10.2196/49400.

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Background Internet-derived data and the autoregressive integrated moving average (ARIMA) and ARIMA with explanatory variable (ARIMAX) models are extensively used for infectious disease surveillance. However, the effectiveness of the Baidu search index (BSI) in predicting the incidence of scarlet fever remains uncertain. Objective Our objective was to investigate whether a low-cost BSI monitoring system could potentially function as a valuable complement to traditional scarlet fever surveillance in China. Methods ARIMA and ARIMAX models were developed to predict the incidence of scarlet fever

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Mwijalilege, Sadock Aron, Michael Lucas Kadigi, and Castory Kibiki. "Comparing ARFIMA and ARIMA Models in Forecasting under Five Mortality Rate in Tanzania." Asian Journal of Probability and Statistics 27, no. 1 (2025): 107–21. https://doi.org/10.9734/ajpas/2025/v27i1707.

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Tanzania has been taking various measures to drop the Under-Five Mortality Rate (UFMR), but the pace to meet national and global UFMR targets has been slow. Nevertheless, the decline for the past years has continued to be low as compared to the Sustainable Development Goals (SDGs) target which is set at 25 deaths/1000 live births by 2030. The lack of statistical modeling-based forecast values of UFMR results into setting targets that are not SMART towards the realization of national and international goals of the health sector. Thus, the current study uses both ARFIMA and ARIMA to make forecas

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Morales-Solis, Anabel, Artemio Pérez-López, Martha Elva Ramírez-Guzmán, Teodoro Espinosa-Solares, and Irán Alia-Tejacal. "ARIMAX Modelling: Response of Hass Avocado Respiration Rate to Environmental Factors." Horticulturae 10, no. 7 (2024): 700. http://dx.doi.org/10.3390/horticulturae10070700.

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This research explores how random events influence the respiration rate in Hass avocado beyond deterministic models in order to develop better strategies for extending its shelf life. Understanding these factors can enhance the accuracy of postharvest management strategies. The Autoregressive Integrated Moving Average (ARIMA) model with exogenous variables (ARIMAX) is an alternative stochastic probability model which is capable of modeling complex, externally influenced phenomena such as respiration. This study aimed to elucidate the effect of three exogenous variables, namely temperature, rel

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Usman, Muhammad Idris, Tasi’u Musa, and Auwalu Ibrahim. "ASSESSING THE PERFORMANCE OF ARIMA AND ARFIMA MODELS IN FORECASTING INTERNALLY GENERATED REVENUE OF KADUNA STATE." FUDMA JOURNAL OF SCIENCES 9, no. 6 (2025): 193–201. https://doi.org/10.33003/fjs-2025-0906-3666.

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Internally generated revenue (IGR) is an important source of revenue that can be used to fund public services and infrastructure projects. Accurate forecasting of IGR is essential for effective budgeting and financial planning. This study assessed the performance of ARIMA and ARFIMA models in forecasting internally generated revenue of Kaduna State. The study uses monthly IGR data from January 2003 to December 2023. The stationarity of the data was assessed using Augmented Dickey Fuller (ADF) and Kwiatkowski-Phillips-Schmidt-Shin (KPSS) tests. The findings showed that both ARIMA and ARFIMA mod

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Adewole, Ayoade. "Modeling Long Memory Volatilities of Nigeria Selected Macro Economic Variables with Arfima and Arfima Figarch." Cumhuriyet Science Journal 45, no. 3 (2024): 618–28. http://dx.doi.org/10.17776/csj.1467360.

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The research delved into analysing the stochastic characteristics of Nigeria's Real GDP, the exchange rate of the Naira to US Dollar, and the inflation rate employing Autoregressive fractionally integrated moving average (ARFIMA) and the Autoregressive Fractionally Integrated Moving Average Fractionally Integrated Generalized Autoregressive Conditional Heteroskedasticity (FIGARCH) modelling approach. The ability of the hybrid formation of ARFIMA-FIGARCH model with Nigeria macroeconomic variables in modeling the periodicity of long memory volatilities was examined. ARIMA GARCH method of modelin

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ISMAIL, NUR AFIQAH, NURIN ALYA RAMZI, and Pauline Jin Wee Mah. "FORECASTING THE UNEMPLOYMENT RATE IN MALAYSIA DURING COVID-19 PANDEMIC USING ARIMA AND ARFIMA MODELS." MALAYSIAN JOURNAL OF COMPUTING 7, no. 1 (2022): 982. http://dx.doi.org/10.24191/mjoc.v7i1.14641.

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The unemployment issue is one of the most common problems faced by many countries around the world. The unemployment rates in developed countries often fluctuate throughout time. Similarly, Malaysia is also affected by the inconsistent unemployment rate especially during the COVID-19 pandemic. Therefore, in order to understand the trend better, ARIMA and ARFIMA were used to model and forecast the unemployment rate in Malaysia in this study. The dataset on the unemployment rate in Malaysia from January 2010 until July 2021 was obtained from Bank Negara Malaysia (BNM) official portal. The best t

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Ilmani, Erdanisa Aghnia, Fida Fariha Amatullah, Khairil Anwar Notodiputro, Yenni Angraini, and Laily Nissa Atul Mualifah. "THE PERFOMANCE OF THE ARIMAX MODEL ON COOKING OIL PRICE DATA IN INDONESIA." BAREKENG: Jurnal Ilmu Matematika dan Terapan 19, no. 2 (2025): 819–28. https://doi.org/10.30598/barekengvol19iss2pp819-828.

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Forecasting is crucial for planning, particularly in addressing potential issues. While ARIMA models are commonly used for time series forecasting, they may need more accuracy by overlooking external factors. The ARIMAX model, which incorporates exogenous variables, is employed to enhance accuracy. This study applies the ARIMAX model to forecast cooking oil prices in Indonesia, known for its complex patterns. Using data from the Directorate General of Domestic Trade and Price Stability (2024), the research highlights fluctuating cooking oil prices from 2010 to 2023 every month. Both ARIMA and

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Kaveh, A. Adli. "Forecasting Steel Prices Using ARIMAX Model: A Case Study of Turkey." International Journal of Business Management and Technology 4, no. 5 (2023): 62–68. https://doi.org/10.5281/zenodo.7668652.

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Steel prices for Turkey as a major steel producer and exporter have substantial importance to be predicted. The ARIMA model with explanatory variables is used to assess the out-of-sample forecast accuracy with the univariate ARIMA as a benchmark. The results revealed that despite expectations, The ARIMA model with explanatory variables could not perform superior results comparing ARIMA models in a 6-month forecast horizon.

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Maku, T. O., M. U. Adehi, and M. O. Adenomon. "Modeling and forecasting electricity consumption in Nigeria using Arima and Arimax time series models." Science World Journal 18, no. 3 (2023): 414–21. http://dx.doi.org/10.4314/swj.v18i3.14.

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This study compared the extrapolation strengths of two models: Autoregressive Integrated Moving Average (ARIMA) and Autoregressive Integrated Moving Average with an Exogenous Variable (ARIMAX) in the forecast of Nigeria's electricity consumption. Annual data on power generation and consumption from the Central Bank of Nigeria statistical bulletin for 2006 and 2016 over a 51-year period (1970-2020) was used. Industrial and residential electricity consumptions were examined for possible unit roots (non-stationarity) using the Augmented Dickey-Fuller test approach. The ADF test result showed that

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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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Sahu, Chowa Ram, Satyananda Basak, and Deb Sankar Gupta. "Long Memory Time-series Model (ARFIMA) Based Modelling of Jute Prices in the Samsi Market of Malda District, West Bengal." Journal of Scientific Research and Reports 30, no. 6 (2024): 600–614. http://dx.doi.org/10.9734/jsrr/2024/v30i62078.

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The objective of this paper is modeling and forecasting the weekly jute prices of Samsi market in the Malda district of West Bengal in the presence of long memory process. The long memory behavior of series is investigated by the ACF plot and Hurst R/S analysis. A fractionally integrated autoregressive moving-average (ARssFIMA) model is fitted using 668 weekly data (January 2009-November 2022). This study shows the efficiencies of the Hurst exponent, GPH, Smoothed periodogram, Local Whittle, and Wavelet methods used to estimate the fractional difference parameter in the ARFIMA model. Furthermo

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Lamboni, Batablinlè, and Agnidé Emmanuel Lawin. "Time series analysis and forecasting of Streamflow at Nangbeto dam in Mono Basin using stochastic approaches." World Journal of Advanced Research and Reviews 23, no. 2 (2024): 754–62. https://doi.org/10.5281/zenodo.14844761.

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Accurate prediction of the streamflow has a significantly importance in water resources management. In this study, two time series models, Autoregressive Moving Average model (ARMA) and Autoregressive Integrated Moving Average model (ARIMA) are used for predicting streamflow based on observed monthly streamflow data from 2000 to 2020. The statistics related to first 16 years were used to train the models and last 5 years (2016-2020) were used to forecast. The accuracy of the models was assessed using statistical metrics such as the Nash efficiency (NE), the Root Mean Square Error (RMSE) and me

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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 (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 weighti

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Mohd Khairi, Syahidah, and Izzatdin Abdul Aziz. "DOMESTIC WATER CONSUMPTION FORECASTING WITH SOCIODEMOGRAPHIC FEATURES USING ARIMA AND ARIMAX: A CASE STUDY IN MALAYSIA." Platform : A Journal of Science and Technology 5, no. 1 (2022): 16. http://dx.doi.org/10.61762/pjstvol5iss1art14919.

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Domestic water consumption in Malaysia exceeds the recommendation by World Health Organisation (WHO) and the target by National Water Services Commission (SPAN). This could cause problems to future water supply, especially due to climate change and sanitation requirements during the pandemic. Domestic water consumption forecasting for each state proposed in this paper could be used to formulate targeted strategies for water management and water conservation awareness. Time series annual data between 2010 to 2019 obtained from Malaysian government portals were used to forecast domestic water co

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Li, Kairui. "Prediction of atmospheric temperature in Sacramento area based on ARIMA and ETS models." Theoretical and Natural Science 51, no. 1 (2024): 165–71. http://dx.doi.org/10.54254/2753-8818/51/2024ch0197.

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Abstract. As global temperatures increased by 1.1 Celsius degrees, there has unprecedented shifts in climate systems. With the rising impact of global warming, exploring the warming trend helps to better understand and maintain the local environment and economy. This study focuses on predicting atmospheric temperature in the Sacramento area using ARIMA and ETS models. The research uses the temperature data from Sacramento Airport's Automated Surface Observing System (ASOS) and explores the prediction performance of ARIMA, ETS, and ARIMAX models in predicting daily average temperatures. The res

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Ali, Abdul-malik, and Joseph Mushi. "Investigation of a Suitable Hybrid Time Series Model for Predicting Clove Price." Journal of ICT Systems 1, no. 2 (2023): 1–16. http://dx.doi.org/10.56279/jicts.v1i2.36.

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The prediction of clove prices in domestic markets is affected by non-linear factors, including the monopoly market operational environment. Most of the hybrid time series models used in the prediction of crop prices do not consider the monopoly market operational environment as a nonlinear factor. This study investigated a suitable hybrid time series model for predicting clove price under the monopoly market in Zanzibar, Tanzania. The study conducted desk reviews on existing hybrid time series models and realize that ARIMA-ANN, ARIMA-SVM, ARIMAX-ANN, and SARIMA-NARNN are the most common and e

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Tchoketch-Kebir, Hacene, and Abderazak Madouri. "Research Leadership and High Standards in Economic Forecasting: Neural Network Models Compared with Etalon ARIMA Models." Business Ethics and Leadership 8, no. 1 (2024): 220–33. http://dx.doi.org/10.61093/bel.8(1).220-233.2024.

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Maintaining high standards in socio-economic research and achieving leadership positions in scientific circles requires a scientist to have a perfect command of mathematical tools developing accurate forecasts. Traditional forecasting methods typically involve fitting data to a pre-established relationship between dependent and independent variables, often making specific assumptions about a stochastic process. In contrast, machine learning presents an alternative approach to statistical analysis and forecasting, emphasising a data-driven methodology that does not assume any predefined statist

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Kaushik, Surbhi, and Nitin Bhardwaj. "Integrating Climatic Data for Mustard Yield Forecasting in Haryana: ARIMA vs ARIMAX Models." International Journal of All Research Education and Scientific Methods 12, no. 12 (2024): 1581–87. https://doi.org/10.56025/ijaresm.2024.1212241581.

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Accurate crop yield forecasting is crucial for making informed agricultural policy decisions regarding importexport, storage, distribution, pricing, and other factors. This study focuses on developing a forecasting methodology for mustard yield in the western region of Haryana, India, specifically in the districts of Hisar, Sirsa, and Bhiwani. Two statistical models, ARIMA (Auto-Regressive Integrated Moving Average) and ARIMAX (Auto-Regressive Integrated Moving Average with Exogenous Variables), are used for forecasting mustard yield, with climate data (such as temperature and rainfall) integr

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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 (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 bet

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KHURAIJAM SHITLE KUMAR and SALAM SHANTIKUMAR SINGH. "Res. ANGRAU 52 (2) 111-121, 2024 *Corresponding Author E-mail i.d: khshitle.phd@gmail.com, Part of Ph.D thesis submitted to Manipur University, Canchipur - 795003 IMPACT OF TURMOIL ON PINEAPPLE PRODUCTION IN MANIPUR: A SCENARIO-BASED FORECAST." Journal of Research ANGRAU 52, no. 2 (2024): 115–25. http://dx.doi.org/10.58537/jorangrau.2024.52.2.12.

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The current turmoil in Manipur State has significantly impacted agriculture, likely to reduce agricultural or horticultural productions including pineapple. Traditional forecasting models typically assume ideal conditions and may not account for such extreme events. This study forecasts pineapple production using Regression, ARIMA, and ARIMAX models, incorporating cultivation area series to train the data. The high correlation (0.8979) between production and cultivation area supports using the area series as a covariate. For scenario-based forecasting, the cultivation area series is generated/

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Prada-Núñez, Raúl, and Cesar Augusto Hernández-Suárez. "Análisis de una serie de tiempo utilizando diseño de experimentos como herramienta de calibración." Eco matemático 6, no. 1 (2015): 50. http://dx.doi.org/10.22463/17948231.459.

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ResumenLas series temporales se usan para estudiar la relación de una variable consigo misma a lo largo del tiempo en intervalos regulares; se consideró el consumo energético de España durante una muestra de 5 días, recurriendo a diversos modelos deterministas se buscaba modelar su comportamiento de la forma más ajustada. Se utiliza el diseño de experimentos para calibrar los parámetros del modelo de HoltWinters validando aquellos efectos que resultan significativos en la minimización del MAPE, con el fin de identificar las Condiciones Operativas Óptimas del modelo. Por último, se evaluan dive

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.., Abdulsalam Elnaeem, and Ani Bin Shabri. "Comparison Between ARIMA and EEMD+ARIMA Models in Forecasting Electricity Consumption." Fusion: Practice and Applications 14, no. 1 (2024): 08–18. http://dx.doi.org/10.54216/fpa.140101.

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Accurate forecasting of future electricity consumption is necessary to create a satisfactory design for an electricity distribution system. To enhance forecasting accuracy, autoregressive integrated moving average (ARIMA) was compared with hybrid of ensemble empirical mode decomposition (EEMD) plus autoregressive integrated moving average (ARIMA) denoted by (EEMD+ARIMA), to know which model is better performing a historical US monthly electricity consumption from DEC-2000 to SEP-2022 were used. The data were divided into training set (90%) and testing set (10%) to insure the model accuracy. Th

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Mah, P. J. W., N. A. M. Ihwal, and N. Z. Azizan. "FORECASTING FRESH WATER AND MARINE FISH PRODUCTION IN MALAYSIA USING ARIMA AND ARFIMA MODELS." MALAYSIAN JOURNAL OF COMPUTING 3, no. 2 (2018): 81. http://dx.doi.org/10.24191/mjoc.v3i2.4887.

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Malaysia is surrounded by sea, rivers and lakes which provide natural sources of fish for human consumption. Hence, fish is one source of protein supply to the country and fishery is a sub-sector that contribute to the national gross domestic product. Since fish forecasting is crucial in fisheries management for managers and scientists, time series modelling can be one useful tool. Time series modelling have been used in many fields of studies including the fields of fisheries. In a previous research, the ARIMA and ARFIMA models were used to model marine fish production in Malaysia and the ARF

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Wu, Chien Ho. "ARIMA Models are Clicks Away." Applied Mechanics and Materials 411-414 (September 2013): 1129–33. http://dx.doi.org/10.4028/www.scientific.net/amm.411-414.1129.

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It is often the case that managers and social scientists are called to deal with time series. Time series analysis usually involves a study of the components of the time series and finding models that permit statistical inferences and predictions. ARIMA models are, in theory, the most general class of models for forecasting a time series. The commonly known Box-Jenkins approach to ARIMA model building is an iterative process. To facilitate the iterative process and to relieve the boredom of computational errands, we have developed an assistor for building ARIMA models. The assistor is implemen

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Al-Qazzaz, Redha Ali, and Suhad A. Yousif. "High performance time series models using auto autoregressive integrated moving average." Indonesian Journal of Electrical Engineering and Computer Science 27, no. 1 (2022): 422. http://dx.doi.org/10.11591/ijeecs.v27.i1.pp422-430.

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Forecasting techniques have received considerable interest from both researchers and academics because of the unique characteristics of businesses and their influence on several areas of the economy. Most academics utilize the autoregressive integrated mov ing average (ARIMA) approach to forecasting the future. However, researchers face challenges, such as analyzing the data and selecting the appropriate ARIMA parameters, especially with large datasets. This study investigates the use of the automatic ARIMA (Auto ARIMA) function for forecasting Brent oil prices. It demonstrates the benefits of

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Al-Qazzaz, Redha Ali, and Suhad A. Yousif. "High performance time series models using auto autoregressive integrated moving average." Indonesian Journal of Electrical Engineering and Computer Science 27, no. 1 (2022): 422–30. https://doi.org/10.11591/ijeecs.v27.i1.pp422-430.

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Forecasting techniques have received considerable interest from both researchers and academics because of the unique characteristics of businesses and their influence on several areas of the economy. Most academics utilize the autoregressive integrated moving average (ARIMA) approach to forecasting the future. However, researchers face challenges, such as analyzing the data and selecting the appropriate ARIMA parameters, especially with large datasets. This study investigates the use of the automatic ARIMA (Auto ARIMA) function for forecasting Brent oil prices. It demonstrates the benefits of

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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 (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

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Goyal, Megha, Subodh Agarwal, Suman Ghalawat, and Joginder Singh Malik. "ARIMA and ARIMAX Analysis on the Effect of Variability of Rainfall, Temperature on Wheat Yield in Haryana." Indian Journal of Extension Education 60, no. 1 (2024): 95–99. http://dx.doi.org/10.48165/ijee.2024.60118.

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The national and state governments require crop production forecasts to make a variety of policy decisions on import-export, storage, distribution, price, and other factors. This article presents a pre-harvest forecasting method specially developed for crops grown in the western region of Haryana (India). The western region includes Hisar, Sirsa, and Bhiwani districts. For crop forecasting in the Hisar, Sirsa, and Bhiwani regions ARIMA and ARIMAX models have been framed. For the development of the ARIMAX model, climate data during the growing season of the crop were used as input along with th

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Hoque, Md Abrarul, Asib Ahmmed Apon, Md Arafat Hassan, Sajal Kumar Adhikary, and Md Ariful Islam. "Enhanced Forecasting of Groundwater Level Incorporating an Exogenous Variable: Evaluating Conventional Multivariate Time Series and Artificial Neural Network Models." Geographies 5, no. 1 (2024): 1. https://doi.org/10.3390/geographies5010001.

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Continuous and uncontrolled extraction of groundwater often creates tremendous pressure on groundwater levels (GWLs). As a part of sustainable planning and effective management of water resources, it is crucial to assess the existing and forecasted GWL conditions. In this study, an attempt was made to model and forecast GWL using artificial neural networks (ANNs) and multivariate time series models. Autoregressive integrated moving average (ARIMA) and ARIMA models incorporating exogenous variables (ARIMAX) were adopted as the time series models. GWL data from five monitoring wells from the stu

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Tretiakov, I. "INTELLIGENT MODELS FOR DEMAND FORECASTING USING AI." SCIENTIFIC-DISCUSSION, no. 95 (December 16, 2024): 28–30. https://doi.org/10.5281/zenodo.14498995.

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This article examines modern demand forecasting methods using intelligent models, including machine learning (ML) algorithms, deep learning, and statistical time series models. The study analyzes the effectiveness of various approaches, such as neural networks, ensemble methods, and time series models (e.g., ARIMA and ARIMAX), in demand forecasting across different economic sectors. Based on real data analysis, the accuracy and applicability of the proposed models are assessed, highlighting the advantages and limitations of intelligent models compared to traditional forecasting methods.

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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, t

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Mehmood, Qaisar, Maqbool Hussain Sial, Muhammad Riaz, and Berihan R. Elemary. "Optimum Rice Prediction from Conventional, Neural Network and Hybrid models." INTERNATIONAL JOURNAL OF AGRICULTURAL AND STATISTICAL SCIENCES 20, no. 01 (2024): 45. http://dx.doi.org/10.59467/ijass.2024.20.45.

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The objective of study was to propose the appropriate model for forecasting the area and production of rice crops in Pakistan. The data for the rice area and production was taken for the agriculture statistics from 1947 to 2020 from the official website, Ministry of Finance, Government of Pakistan. The conventional ARIMA methodology was applied initially to forecast the rice area and production by using the proposed ARIMA(1,1,0) model. Then ARIMA, ETS, TBATS, Artificial Neural Network (ANN) and ARIMA-ETS, ARIMA-TBATS and ARIMA-ANN hybrid model were compared. It was observed that ARIMA(1,1,0) m

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Bordun, Mykhailo, and Oleh Salapak. "MATHEMATICAL MODELS FOR THE ANALYSIS AND FORECASTING OF RIVER WATER POLLUTION USING THE MULTIFRACTAL METHOD." Computer Design Systems. Theory and Practice 7, no. 1 (2025): 310–18. https://doi.org/10.23939/cds2025.01.310.

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This paper explores multifractal analysis for the selected time series water pollution data set and further prediction based on BOD measure with ARFIMA-based fractal model. MFDFA multifractal algorithm is applied for estimating the fractal differentiation parameter of the ARFIMA. The obtained results are compared with similar obtained with autoregressive ARIMA model and basic ARFIMA fractal model. The study reveals an enhancement in accuracy with the use of combination of multifractal analysis and fractal methods for water pollution 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 (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, mul

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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 A

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Mahmad Azan, Atiqa Nur Azza, Nur Faizatul Auni Mohd Zulkifly Mototo, and Pauline Jin Wee Mah. "The Comparison between ARIMA and ARFIMA Model to Forecast Kijang Emas (Gold) Prices in Malaysia using MAE, RMSE and MAPE." Journal of Computing Research and Innovation 6, no. 3 (2021): 22–33. http://dx.doi.org/10.24191/jcrinn.v6i3.225.

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Gold is known as the most valuable commodity in the world because it is a universal currency recognized by every single bank across the globe. Thus, many people were interested in investing gold since gold market was always steadier compared to other investment (Khamis and Awang, 2020). However, the credibility of gold was questionable due to the changes in gold prices caused by a variety of circumstances (Henriksen, 2018). Hence, information on the inflation of gold prices were needed to understand the trend in order to plan for the future in accordance with international gold price standards

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SAMEERABANU, Dr P. "Forecasting Onion Production in India Using Arima Models: A Research Study." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 008 (2024): 1–7. http://dx.doi.org/10.55041/ijsrem37087.

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This study explores the application of ARIMA (AutoRegressive Integrated Moving Average) models for forecasting onion production in India. Accurate forecasting of agricultural production is essential for effective planning and decision-making in the agricultural sector. ARIMA models, which integrate autoregression, differencing, and moving average components, offer a robust methodology for time series forecasting. This study aims to explore the application of ARIMA models in forecasting onion production in India. By utilizing historical production data, the study will identify suitable ARIMA pa

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Peng, Zhang, Farman Ullah Khan, Faridoon Khan, et al. "An Application of Hybrid Models for Weekly Stock Market Index Prediction: Empirical Evidence from SAARC Countries." Complexity 2021 (December 6, 2021): 1–10. http://dx.doi.org/10.1155/2021/5663302.

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The foremost aim of this research was to forecast the performance of three stock market indices using the multilayer perceptron (MLP), recurrent neural network (RNN), and autoregressive integrated moving average (ARIMA) on historical data. Moreover, we compared the extrapolative abilities of a hybrid of ARIMA with MLP and RNN models, which are called ARIMA-MLP and ARIMA-RNN. Because of the complicated and noisy nature of financial data, we combine novel machine-learning techniques such as MLP and RNN with ARIMA model to predict the three stock market data. The data used in this study are taken

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Snyder, Ralph D., J. Keith Ord, and Anne B. Koehler. "Prediction Intervals for ARIMA Models." Journal of Business & Economic Statistics 19, no. 2 (2001): 217–25. http://dx.doi.org/10.1198/073500101316970430.

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Dekleva, J., and N. Rožić. "Forecasting: Arima or Kalman Models." IFAC Proceedings Volumes 18, no. 5 (1985): 649–56. http://dx.doi.org/10.1016/s1474-6670(17)60634-7.

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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 (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 v

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Chen, Yun-Peng, Le-Fan Liu, Yang Che, et al. "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 (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

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Derbentsev, Vasily, Natalia Datsenko, Olga Stepanenko, and Vitaly Bezkorovainyi. "Forecasting cryptocurrency prices time series using machine learning approach." SHS Web of Conferences 65 (2019): 02001. http://dx.doi.org/10.1051/shsconf/20196502001.

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This paper describes the construction of the short-term forecasting model of cryptocurrencies’ prices using machine learning approach. The modified model of Binary Auto Regressive Tree (BART) is adapted from the standard models of regression trees and the data of the time series. BART combines the classic algorithm classification and regression trees (C&RT) and autoregressive models ARIMA. Using the BART model, we made a short-term forecast (from 5 to 30 days) for the 3 most capitalized cryptocurrencies: Bitcoin, Ethereum and Ripple. We found that the proposed approach was more accurate th

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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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Wu, Minglang, Htwe Ko, and Chukiat Chaiboonsri. "Forecasting China's Air Cargo Volume by ARIMA vs Holt-Winters." International Journal of Science and Social Science Research 2, no. 3 (2024): 123–1228. https://doi.org/10.5281/zenodo.14171114.

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This study compares the accuracy of ARIMA and Holt-Winters forecasting models in predicting China’s air cargo volume for a 5-year ahead horizon. Using time series data from 2000 to 2023. ARIMA model emphasizes autocorrelation within the data, while Holt-Winters model accounts for level and trend components, excluding seasonal effects. Both models are applied using univariate forecasting approaches to evaluate their performance in predicting future air cargo volumes. A comparison of the forecasting models for China's air cargo volume shows that the ARIMA (1,1,0) model outperforms the Holt

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Rizvi, Mohd Faizan. "ARIMA Model Time Series Forecasting." International Journal for Research in Applied Science and Engineering Technology 12, no. 5 (2024): 3782–85. http://dx.doi.org/10.22214/ijraset.2024.62416.

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Abstract: Time series forecasting is a critical component in various fields such as finance, economics, meteorology, and engineering. Among the multitude of methods available for time series forecasting, the Autoregressive Integrated Moving Average (ARIMA) model stands out for its simplicity and effectiveness. This paper provides a comprehensive review of ARIMA models, focusing on their application in forecasting time series data. We begin with an overview of time series analysis and the theoretical foundations of ARIMA models. Subsequently, we delve into the process of building and fitting AR

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