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Journal articles on the topic 'ARIMA model30'

Author: Grafiati

Published: 11 September 2021

Last updated: 30 July 2025

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1

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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Dou, Haoyuan. "Forecasting the JPY/USD Exchange Rate Using the ARIMA Model." Advances in Economics, Management and Political Sciences 193, no. 1 (2025): 246–54. https://doi.org/10.54254/2754-1169/2025.lh24938.

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In this paper, based on the monthly JPY/USD exchange rate data from June 2010 to June 2024, a systematic time series analysis and forecasting is carried out by using the ARIMA (0,1,0) (0,0,2) [12] model. Firstly, the study ensures the data smoothness by first order differencing and checked with ADF test, and then determines the model structure by using auto.arima method. The model diagnosis shows that the residuals are white noise (Qualified by Ljung-Box Test) and the model fit performs well. By forecasting the exchange rate from July to December 2024 and comparing it with the actual data, the

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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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Wan, Hongyu. "Chinese Housing Prices Prediction using Autoregressive Integrated Moving Average Model." Highlights in Science, Engineering and Technology 94 (April 26, 2024): 91–96. http://dx.doi.org/10.54097/v85rf878.

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This paper investigates the application of the Autoregressive Integrated Moving Average (ARIMA) model to predict future trends in Chinese housing prices. The Chinese real estate market, characterized by its volatility, especially during the post-COVID-19 period, presents a complex environment for buyers and investors. The paper investigates how the ARIMA model is employed to make informed predictions in this uncertain market. Although it has some limitations, such as a heavy reliance on historical data and insensitivity to unexpected macroeconomic shifts, the ARIMA model offers a structure for

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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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Gu, Baorun. "Forecasting the Index of Nikkei 225 Based on ARIMA Model." Advances in Economics, Management and Political Sciences 147, no. 1 (2025): 74–80. https://doi.org/10.54254/2754-1169/2024.ga19124.

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In this study, the ARIMA (1, 1, 0) model forecasts the Nikkei 225 index from September 2022 to September 2024. Initially, the difference operation and ADF test are used to guarantee that the data is smooth, and then an optimal ARIMA model is chosen. Meanwhile, ARIMA (1, 1, 0) is considered the most appropriate model based on AIC and BIC criteria. The model shows an excellent prediction ability, particularly over the next 30 days, indicating that the market trend is consistent. However, the confidence intervals increasingly broaden with time, increasing in forecasting uncertainty, particularly

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Villaren, M. Vibas, and R. Raqueño Avelina. "A Mathematical Model for Estimating Retail Price Movements of Basic Fruit and Vegetable Commodities Using Time Series Analysis." International Journal of Advance Study and Research Work 2, no. 7 (2019): 01–05. https://doi.org/10.5281/zenodo.3333529.

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<strong><em>Prices of basic agricultural commodities in the market truly concern the entire populace in a region or country. They directly affect the consumers, farmers, traders, entrepreneurs, and even the government and policymakers. Developing a mathematical model in relation to the retail price movements of these basic agricultural commodities could possibly help every concerned individual with regard to economic matters as well as in planning the future. Specifically, the study included basic commodities such as fruits (banana and mango) and vegetables (tomato, cabbage, and pechay) in the

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KOZICKI, Bartosz. "THE IMPLEMENTATION OF ARIMA MODEL FOR THE FORECAST OF IMPORTATION OF GOODS TO POLAND IN 2019." Systemy Logistyczne Wojsk 50, no. 1 (2019): 127–41. http://dx.doi.org/10.37055/slw/129236.

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W artykule poruszony został problem z zakresu analizy i oceny danych dotyczących importu towarów do Polski w latach 2011-2018 w milionach ton oraz próba przeprowadzenia prognozowania eksportu w Polsce na czternaście przyszłych okresów modelem ARIMA. Badania rozpoczęto od analizy i oceny danych dotyczących importu towarów w milionach ton w Polsce w ujęciu dynamicznym. Następnie na podstawie uzyskanych ocen wybrano model prognostyczny ARIMA, a następnie zbudowano dwa modele uczące typu ARIMA. Zbudowane modele ARIMA zostały poddane analizie i ocenie. Wybrano najlepszy. Na jego podstawie wykonano

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Jin, Xi, and Hyuntai Kim. "Exploring the Business-Culture Relationship with Box-Jenkins ARIMA Analysis for Forecasting the Path and Future Prospects of the Popular Music Industry." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 6 (2023): 372–79. http://dx.doi.org/10.17762/ijritcc.v11i6.7726.

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Time series analysis plays a crucial role in understanding and predicting the path and future prospects of industries, including the popular music industry. This paper constructed an Box-Jenkins ARIMA (BJ-ARIMA) methodology to analyze the time series data in the popular music industry, with a focus on the relationship between business and culture. By employing the Box-Jenkins approach, BJ-ARIMA forecast future trends and make informed predictions about the development of the industry. Identification, estimation, and diagnostic testing using the BJ-ARIMA framework are the three main components

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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 (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

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Pranjal, Tarte, and Sawant Pratiksha. "An Exploration of the ARIMA Model for Time Series Prediction and Analysis." International Journal of Advance and Applied Research S6, no. 22 (2025): 1120–23. https://doi.org/10.5281/zenodo.15542599.

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<em>This study explores the application of the Auto Regressive Integrated Moving Average (ARIMA) model for time series forecasting. ARIMA is a widely used statistical technique that combines auto regression, differencing, and moving average components to model and predict future values in a time-dependent dataset. The model is particularly effective for datasets that exhibit trends and require stationarity through differencing. This research demonstrates the ARIMA model's capability to analyse historical data, identify underlying patterns, and produce accurate forecasts. By applying the ARIMA

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Xu, Haihui, Zhiyuan Ge, and Wenjie Ao. "Research on Climate Change Prediction based on ARIMA Model and its Impact on Insurance Industry Decision-Making." Frontiers in Computing and Intelligent Systems 8, no. 1 (2024): 1–5. http://dx.doi.org/10.54097/3r7nkd35.

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This research delves into the application of the Autoregressive Integrated Moving Average (ARIMA) model for predicting climate change and its subsequent implications for decision-making within the insurance industry. The study introduces a comprehensive approach to forecast climatic variables such as temperature, rainfall, and relative humidity, which are critical factors in assessing insurance risks and formulating underwriting strategies. The ARIMA model, recognized for its efficacy in time series analysis, is employed to capture the seasonal patterns and trends in climatic data. The model i

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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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Hong, Kongrui, Xiaoqi Wang, and Liming Xu. "Research on price forecasting and trading strategy based on data insight." BCP Business & Management 22 (July 15, 2022): 232–38. http://dx.doi.org/10.54691/bcpbm.v22i.1234.

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The goal of establishing the model in this paper is to find the historical price patterns of gold and bitcoin according to the data provided. The purpose is to maximize returns under various market constraints and avoid loss risk as much as possible. Traders provide the best trading strategy. In this paper, two models are established: model 1: price prediction model based on ARIMA; Model 2: quantitative trading strategy model based on dynamic programming. For Model 1, a classical time series modeling approach based on stock forecasting was used: the ARIMA price forecasting model. The model's v

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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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Zahra, Moslemi, Rehome Samantha, Clark Logan, et al. "Comprehensive forecasting of California's energy consumption: A multi-source and sectoral analysis using ARIMA and ARIMAX models." World Journal of Advanced Research and Reviews 22, no. 2 (2024): 484–97. https://doi.org/10.5281/zenodo.14554886.

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California’s significant role as the second-largest consumer of energy in the United States underscores the importance of accurate energy consumption predictions. With a thriving industrial sector, a burgeoning population, and ambitious environmental goals, the state’s energy landscape is dynamic and complex. This paper presents a comprehensive analysis of California’s energy consumption trends and provides detailed forecasting models for different energy sources and sectors. The study leverages ARIMA and ARIMAX models, considering both historical consumption data and exogeno

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Li, Mingcan. "Research on Stock Price Forecasting Based on the ARIMA-GARCH Model: A Case Study of Apeloa Pharmaceutical." Advances in Economics, Management and Political Sciences 193, no. 1 (2025): None. https://doi.org/10.54254/2754-1169/2025.lh24321.

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This study primarily investigates the application of the ARIMA-GARCH model in forecasting pharmaceutical industry stocks, with Apeloa Pharmaceutical chosen as the sample stock and the sample period spanning from 2020 to 2024. Initially, this research determines the order of the ARIMA model using information criteria, and subsequently conducts joint volatility modeling on the residual series of the ARIMA model. The modeling results demonstrate that all parameters exhibit favorable statistical significance. Based on the established model, backward forecasting is performed, yielding the MAPE of 3

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Han, Yicheng, Nianhao Li, Leyu Qian, and Qinlin Yu. "Analysis of AIDS Transmission Based on ARIMA Model." Highlights in Science, Engineering and Technology 88 (March 29, 2024): 253–59. http://dx.doi.org/10.54097/j9gthe66.

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In response to the ongoing challenge of infectious diseases like AIDS, infectious disease experts have turned to mathematical modeling. One such model, the ARIMA (Auto Regressive Integrated Moving Average) model, has proven effective in predicting disease spread. ARIMA relies on historical data to forecast future transmission rates, enabling proactive measures to be taken. This study utilizes the ARIMA model to predict the future trajectory of AIDS cases in Guangdong Province, China, based on historical data. Initial data analysis reveals a non-linear growth pattern in AIDS cases, emphasizing

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Inyang, E. J., E. P. Clement, M. A. Raheem, and E. M. Usungurua. "Non-seasonal ARIMA modeling of stroke incidence." World Journal of Applied Science & Technology 16, no. 1 (2025): 134–41. https://doi.org/10.4314/wojast.v16i1.134.

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This study employs the ARIMA Box-Jenkins methodology to model monthly stroke incidence data from January 2011 to December 2021. A non-seasonal ARIMA (p,d,q) model is fitted, with ARIMA (1,1,1) identified as the optimal model based on its lowest BIC (=781.5278) and AIC (=772.9022) values compared to alternative models. The Ljung-Box statistic (19.931, df = 22, p = 0.5885 > 0.05) affirms the model's suitability. Projections derived from the fitted model indicate a discernible trend towards increasing stroke incidence. Given the anticipated socioeconomic implications, particularly regarding em

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GRZELAK, Małgorzata. "APPLICATION OF ARIMA MODEL FOR FORECASTING PRODUCTION QUANTITY IN ENTERPRISE." Systemy Logistyczne Wojsk 50, no. 1 (2019): 93–106. http://dx.doi.org/10.37055/slw/129234.

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Celem przedsiębiorstw produkcyjnych jest zaspokajanie potrzeb klientów, poprzez terminowe wytwarzanie wyrobów zgodnie z popytem występującym na rynku. Powyższe działania umożliwiane są przez prawidłowe sporządzanie prognoz potencjalnych zamówień. W poniższym artykule przedstawiono model ARIMA jako narzędzie wspierające planowanie wielkości produkcji w przedsiębiorstwie. Dokonano również oceny wiarygodności opracowanego modelu poprzez analizę reszt oraz ich autokorelacji i autokorelacji cząstkowych.

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Sokolović, Živko, and Saša Milić. "Electricity Consumption Forecasting using ARIMA and LSTM." Zbornik radova Elektrotehnicki institut Nikola Tesla, no. 00 (2025): 3. https://doi.org/10.5937/zeint0-58547.

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Accurate load forecasting is essential for the reliable and efficient operation of modern power systems. This study presents a comparative analysis of two prominent forecasting models-Autoregressive Integrated Moving Average (ARIMA) and Long Short-Term Memory (LSTM)-to assess their effectiveness in predicting electricity consumption. Both models were developed and fine-tuned through hyperparameter optimization to ensure fair and optimal performance. The evaluation considered predictive accuracy, computational efficiency, and resource usage. While ARIMA demonstrated advantages in inference spee

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Ahmar, Ansari Saleh, and Eva Boj. "Time Series Innovation: Leveraging BetaSutte Models to Enhance Indonesia's Export Price Forecasting." Journal of Applied Science, Engineering, Technology, and Education 7, no. 1 (2025): 29–40. https://doi.org/10.35877/454ri.asci3831.

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This study introduces a novel application of the Modified Trend-Augmented α-Sutte Indicator (BetaSutte) model for forecasting Indonesia's export prices and compares its performance with the traditional ARIMA approach. Accurate export price forecasting is crucial for economic planning, trade policy formulation, and business strategy development in Indonesia's dynamic and globally connected economy. Using monthly export value data from January 2022 to September 2024 obtained from Indonesia's Central Bureau of Statistics (BPS), we examined whether the BetaSutte model's decomposition of trend and

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Fang Liu, Fang Liu. "Financial Statement Analysis Based on RNN-RBM Model." Journal of Electrical Systems 20, no. 1 (2024): 106–23. http://dx.doi.org/10.52783/jes.670.

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Financial statement analysis is a critical component of decision-making for businesses, investors, and financial professionals. To enhance the accuracy and effectiveness of such analysis, this paper introduces the application of an innovative approach known as the Intelligent Swarm Regression ARIMA Model. This advanced model combines the power of swarm intelligence with ARIMA (AutoRegressive Integrated Moving Average) time series forecasting, offering a robust methodology for predicting and analyzing key financial metrics. The study begins by providing an overview of the Intelligent Swarm Regr

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Abdelmgeed, Ahmed, Ahmed Mohamed Zaki, and Marwa Adel Soliman. "An Evaluation of ARIMA and Persistence Models in IoT-Driven Smart Homes." Journal of Artificial Intelligence and Metaheuristics 6, no. 2 (2023): 08–15. http://dx.doi.org/10.54216/jaim.060201.

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Commencing with the transformative fusion of Smart Home and Internet of Things (IoT) technologies, this study scrutinizes the efficacy of predictive modeling approaches, specifically the autoregressive integrated moving average (ARIMA) and persistence algorithms. The primary focus lies in their potential for forecasting and optimizing energy consumption dynamics within the intricate framework of smart homes. The investigation reveals a nuanced comparison between the proposed ARIMA and conventional Persistence models. Smart Home, emblematic of innovative living, integrates seamlessly with IoT,

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Wong, Wei Ming, Mohamad Yusry Lee, Amierul Syazrul Azman, and Lew Ai Fen Rose. "Development of Short-term Flood Forecast Using ARIMA." International Journal of Mathematical Models and Methods in Applied Sciences 15 (April 5, 2021): 68–75. http://dx.doi.org/10.46300/9101.2021.15.10.

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The aim of this study is to use the Box-Jenkins method to build a flood forecast model by analysing real-time flood parameters for Pengkalan Rama, Melaka river, hereafter known as Sungai Melaka. The time series was tested for stationarity using the Augmented Dickey-Fuller (ADF) and differencing method to render a non-stationary time series stationary from 1 July 2020 at 12:00am to 30th July 2020. A utocorrelation (ACF) and partial autocorrelation (PACF) functions was measured and observed using visual observation to identify the suitable model for water level time series. The parameter Akaike

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Wang, Shihan. "Forecasting Amazons Quarterly Net Sales Based on Time Series." Advances in Economics, Management and Political Sciences 143, no. 1 (2024): 98–104. https://doi.org/10.54254/2754-1169/2024.ga18961.

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This paper presents an in-depth analysis of the Autoregressive Integrated Moving Average (ARIMA) model for forecasting Amazons quarterly net sales, using historical data from 2007 to 2019. The models ability to handle trends and seasonality in time series data is highlighted. The study outlines the data transformation process, including log transformations and differencing, to ensure stationarity before model development. Four potential ARIMA models were constructed based on the observed autoregressive and moving average characteristics. The ARIMA(3,1,4) model was ultimately selected for its o

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Zhang, Weixin. "S&P 500 Index Price Prediction Based on ARIMA Model." Advances in Economics, Management and Political Sciences 147, no. 1 (2025): 156–61. https://doi.org/10.54254/2754-1169/2024.ga19199.

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This study investigates the use of the ARIMA model to forecast S&P 500 index price fluctuations. Finding out if ARIMA is a good fit for financial time series forecasting with an emphasis on trend and volatility detection is the main goal. The study employs historical data from January to May 2024, using ARIMA to forecast future trends. Part of the inquiry includes a comprehensive assessment of the model's performance, including its ability to spot short-term fluctuations and anticipate accurately. Data detection such as white noise and seasonality is carried out to determine whether the mo

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Rahman, Mohammad Mukhlesur, Mohammad Amirul Islam, Md Golam Mahboob, Nur Mohammad, and Istiak Ahmed. "Forecasting of Potato Production in Bangladesh using ARIMA and Mixed Model Approach." Scholars Journal of Agriculture and Veterinary Sciences 9, no. 10 (2022): 136–45. http://dx.doi.org/10.36347/sjavs.2022.v09i10.001.

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A time series model is used to forecast future values by identifying patterns of historical movement of a variable. This study attempted to develop the best potato predicting model in Bangladesh using BBS provided secondary annual data on area and production of potatoes in Bangladesh from 1970–71 to 2019–20, using the most recent accessible criteria for selecting a model, such as AIC, BIC, RMSE and others. The ARIMA (0, 2, 2) model is the box-Jenkins ARIMA model with the best selection for forecasting potato output throughout Bangladesh. When considering the area of the potato, the mixed model

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Chen, Qiaojun, Jiale Qin, Tianyu Zhang, Jiahe Guang, and Jiahao Gao. "Prediction of building settlement amount based on the ARIMA-LSTM-GXBoost combined model." Applied and Computational Engineering 74, no. 1 (2024): 231–38. http://dx.doi.org/10.54254/2755-2721/74/20240481.

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Accurate prediction of building settlement amounts is crucial for ensuring the safety of building structures and human lives. Addressing issues such as the scarcity of building settlement measurement data and the lack of weighting in combined prediction model results, which have not yet led to further improvements in prediction accuracy, this study first selected building CJ06 in Yizheng City, Yangzhou, as the research subject. Polynomial fitting was applied to the obtained non-equidistant time series data, and the fitting data with a preferable polynomial order was chosen to replace the origi

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Hassan, Raheel, Kanishk Bhandari, Rajkumar Singh, Yokshit Sankhyan, Deepanshu Kumar, and Yash Gupta. "Forecasting US Inflation Trends: Insights from Time Series Analysis." International Journal for Research in Applied Science and Engineering Technology 12, no. 4 (2024): 3104–11. http://dx.doi.org/10.22214/ijraset.2024.60658.

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Abstract: In our research, we dive into the world of forecasting US inflation using the ARIMA (Auto-Regressive Integrated Moving Average) model. We crafted a predictive framework by meticulously analyzing data, conducting tests like the Augmented Dickey-Fuller, and crunching metrics such as RMSE and MSE. Our meticulous model selection procedures involve carefully examining many ARIMA configurations to identify the ideal parameters, ensuring robustness and accuracy in forecasting inflation trends over the study period. Our findings show that the ARIMA (0,1,2) model outperforms others, offering

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Xia, Chenxi. "Comparative Analysis of ARIMA and LSTM Models for Agricultural Product Price Forecasting." Highlights in Science, Engineering and Technology 85 (March 13, 2024): 1032–40. http://dx.doi.org/10.54097/8q6nx369.

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The fluctuations in vegetable prices can have an impact on the economy. Machine learning can identify price trend changes. This study investigates the performance of ARIMA and LSTM models in predicting price trends for agricultural products, focusing on greens and lotus roots. The objective was to ascertain the superior model in terms of accurately reflecting market oscillations—a critical aspect for stakeholders in the agricultural sector. The investigation contrasted the ARIMA model's adeptness at detecting linear tendencies against the LSTM's capacity to decode intricate nonlinear dynamics.

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Esther, D. Gutiérrez, Puche Rafael, and Hernández Fernando. "Estimación de casos de COVID-19 en países de Suramérica empleando modelos ARIMA (Autorregresivo Integrado de Promedio Móvil)." Observador del Conocimiento Vol. 5 Nº 3 septiembre – diciembre 2020, no. 2343-6212 (2021): 11–25. https://doi.org/10.5281/zenodo.4768513.

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El objetivo principal de este trabajo es emplear modelos ARIMA para la estimación de nuevos contagios usando datos públicos disponibles para Venezuela y la región suramericana, actualmente foco principal de un segundo brote de la COVID-19. Se realiza la predicción a 30 días del número de casos de Covid-19 en países suramericanos usando los datos públicos disponibles. Se emplearon modelos ARIMA para estimar el impacto de nuevos contagios en las dinámicas de infección para Suramérica. Desde la aparición del prime

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Kozicki, Bartosz, Jarosław Tomaszewski, Grzegorz Mizura, and Andrzej Piotrowski. "Forecasting needs in the supply department using the ARIMA model in terms of economic security." Nowoczesne Systemy Zarządzania 15, no. 4 (2020): 51–63. http://dx.doi.org/10.37055/nsz/133709.

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W artykule wykonano przegląd literatury związanej z planowaniem, wydatkami, potrzebami, prognozowaniem i bezpieczeństwem ekonomicznym. Przeprowadzono analizę i ocenę szeregu czasowego wydatków poniesionych w dziale zaopatrzenia w podmiocie badań. Ocena pozwoliła na dobór modelu ARIMA do prognozowania wydatków (potrzeb) na przyszłość. Uzyskane prognozy zostały poddane analizie i ocenie. Opracowanie kończy się podsumowaniem i wnioskami.

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Zhou, Lele, and Hyang-Sook Lee. "A Comparative Study of Explanatory and Predictive Models in Air Cargo Throughput." Korean Logistics Research Association 32, no. 4 (2022): 47–54. http://dx.doi.org/10.17825/klr.2022.32.4.47.

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Air cargo throughput has played an important role in the contribution of total national commerce volume and regional GDP, by the expansion of the global economy. Previous studies have identified the critical significance of air freight in national or international economic growth. Various models, including linear regression and time-series models, have been applied to analyze and predict the factors influencing air cargo volumes and the trends for future development. However, many models are implemented as a single methodology, but a few studies reviewed and mixed explanatory and predictive mo

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Zhang, Dihan. "Forecasting USA Unemployment Rate Base on ARIMA Model." Advances in Economics, Management and Political Sciences 49, no. 1 (2023): 67–76. http://dx.doi.org/10.54254/2754-1169/49/20230486.

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This paper presents a detailed analysis of unemployment rate forecasting, a critical subject for various stakeholders including policymakers, businesses, and individuals. Amid significant economic events such as the global financial crisis and COVID-19 pandemic, the need for precise unemployment forecasts has become crucial. The research utilizes an Autoregressive Integrated Moving Average (ARIMA) model to analyze US unemployment rate data from 2000 to 2023, sourced from the Federal Reserve Economic Data (FRED). The paper identifies seasonality patterns, executes appropriate data transformatio

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Nabilah Faisal, Ananda, ESM Nababan, Sutarman Sutarman, and Parapat Gultom. "Forecasting The Share Price of PT Merdeka Copper Gold Tbk By Using Arch-Garch Model." Jurnal Sains dan Teknologi Industri 20, no. 2 (2023): 958. http://dx.doi.org/10.24014/sitekin.v20i2.22704.

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This study aims to obtain a forecasting model and compare PT Merdeka Copper Gold Tbk share price using the time series method, ARCH-GARCH model. The data used is historical data for the period December 2021 – December 2022. The initial steps are the stationarity test, identifying the ARIMA model, and checking the heteroscedasticity effect of the best ARIMA model. Then from this model, identify the ARCH-GARCH model. After the model has been formed, compare those models that have been assumed by using the smallest AIC and SBC values and checking the model's heteroscedasticity effect. The last st

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Yang, Mingyue. "Prediction of Medium-duration Subway Passenger Flow Volume based on the ARIMA Model." Transactions on Computer Science and Intelligent Systems Research 5 (August 12, 2024): 159–65. http://dx.doi.org/10.62051/e6bjzx24.

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The subway industry has brought about a significant change in transportation by effectively reducing ground traffic congestion, resulting in an increasing demand for predicting subway passenger flow based on historical data. Researchers are constantly striving to improve the diversity and accuracy of data prediction models. This paper examines the daily passenger flow data of Nanjing Metro in 2023 and makes medium-term predictions using the ARIMA model to explore the feasibility and effectiveness of this approach. ADF, ACF, and PACF tests are conducted on the data to ensure that the parameters

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Sabah, Manfi Redha. "BRAIN. Broad Research in Artificial Intelligence and Neuroscience-The Prediction of the Rate of the Dropout of the Primary Schools Students by Using the Genetic Algorithm." BRAIN. Broad Research in Artificial Intelligence and Neuroscience 9, no. 2 (2018): 198–214. https://doi.org/10.5281/zenodo.1245929.

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In this research, the ARIMA model of the time series has been applied for the prediction of the rate of the dropout of the primary schools for the male and female students during the period (2007-2015) by estimating the autocorrelation and partial coefficients. It shows that the time series is unstable. After estimating autocorrelation and partial coefficients, it manifests that the appropriate ARIMA models (1,1,0) for males and ARIMA (1,1,0) for females and ARIMA (1,1,0) for males and females together. Also, it has been assured that these models are good and give accurate predictions and clos

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Cao, Dingshan. "Forecasting the Real GDP of United Kingdom, Germany and France in Next Three Years." Theoretical and Natural Science 109, no. 1 (2025): 37–41. https://doi.org/10.54254/2753-8818/2025.gl23169.

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Gross Domestic Product (GDP) has been drawn much attention by people because GDP could show the finance situation of a country. Therefore, this study employs the Auto Regressive Integrated Moving Average (ARIMA) model to forecast the real GDP of United Kingdom, Germany, and France for the next three years (2025-2027). Utilizing quarterly real GDP data from 2015 to 2024, the paper identifies optimal ARIMA parameters () for each country and analyzes the implications of the forecasted trends. This study demonstrates the model's effectiveness in capturing economic trends through its interpretable

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Toba, Temitope Bamidele, and Barnabas Adebola Femi. "MODELING VOLATILITY OF NIGERIA STOCK EXCHANGE USING GARCH MODELS." International Journal of Recent Research in Interdisciplinary Sciences (IJRRIS) 11, no. 2 (2024): 45–53. https://doi.org/10.5281/zenodo.11473668.

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<strong>Abstract:</strong> Financial and economic variables fluctuate owing to a variety of causes, including economic conditions, market pressures, government policies, global effects, industry-specific factors, and even random events. Addressing these fluctuations requires the development of accurate forecasting models to help market participants and policymakers adapt to the dynamic nature of stock market volatility. This research models the conditional mean and variance of Nigeria Stock Exchange Banking Index (NGX-BANK) by obtaining the ARIMA model that captures the linear dependency in th

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Garner, Stacy, Yingxian Pan, and Meijia Shi. "Amazon’s Stock Trends Prediction based on ARIMA Model." Highlights in Business, Economics and Management 35 (June 16, 2024): 58–64. http://dx.doi.org/10.54097/g3yrh896.

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The rapid development of e-commerce in recent years has made stock market prediction a challenging yet important task for investors and traders. The ARIMA model has been extensively applied for forecasting in financial time series data. This study applied the ARIMA model to predict the Amazon stock price, utilizing historical stock price data from a period of a decade. The results of the analysis indicated that the ARIMA model can effectively predict short-term stock price movements with a certain level of accuracy. The model managed to encapsulate the predominant trend in the variations of Am

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Aghayev, N. B., and D. Sh Nazarli. "Modelling of non-scheduled air transportation time series based on ARIMA." Civil Aviation High Technologies 27, no. 6 (2025): 8–20. https://doi.org/10.26467/2079-0619-2024-27-6-8-20.

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Forecasting non-scheduled air transportation demand is essential for effective resource allocation, operational planning, and decision-making. In this paper, the use of the ARIMA (Auto Regressive Integrated Moving Average) model for forecasting non-scheduled air transportation is explored. The ARIMA model is a widely employed time series forecasting technique which combines autoregressive (AR), differencing (I), and moving average (MA) components. It has been successfully applied to various fields and can be adapted to capture the patterns and trends in non-scheduled air transportation data. T

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Gonçalves Cas, Carlos. "Application of The ARIMA Model to Forecast the Price of the Commodity Corn." Revista Gestão da Produção Operações e Sistema 11, no. 1 (2018): 263–79. http://dx.doi.org/10.15675/gepros.v13i1.2040.

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Maheshnath, M., R. Vijaya Kumari, K. Suhasini, D. Srinivasa Reddy, and A. Meena. "Forecasting Maize Production in Telangana State Using Arima Model." Archives of Current Research International 24, no. 6 (2024): 223–29. http://dx.doi.org/10.9734/acri/2024/v24i6780.

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The study utilized the Box-Jenkins approach for forecasting maize production in Telangana state. It involved the analysis of 55 years of empirical annual observations of maize production. The autocorrelation (ACF) and partial autocorrelation functions (PACF) were calculated to analyze the data. A suitable Box-Jenkins ARIMA model was fitted, and the validity of the model was examined using conventional statistical methods. Lastly, the next three years' worth of maize production was predicted using the autoregressive integrated moving average model's forecasting capability.

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Xue, Qijun. "Stock Price Forecasting Based on ARIMA Model an Example of Cheung Kong Hutchison Industrial Co." Highlights in Business, Economics and Management 10 (May 9, 2023): 425–30. http://dx.doi.org/10.54097/hbem.v10i.8134.

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Previous studies in stock prediction mainly researched the ARIMA model, however, scant studies take Hong Kong stock price prediction as a data source for this model. The present study takes Cheung Kong Hutchison Industrial Co. as an example. With conducting descriptive analysis and the ARIMA model, this study goes through four steps: 1) Stationarity Processing: the time series data are pre-processed with differential operations to be stationary, and the series are tested for stationarity by the unit root (ADF) test. 2) Order determination: the order can be observed by drawing autocorrelation a

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Raza, Ahmed, Guangjie Liu, James Msughter Adeke, Jie Cheng, and Danish Attique. "Passenger Flow Prediction Method based on Hybrid Algorithm: Intelligent Transportation System." European Journal of Applied Science, Engineering and Technology 2, no. 1 (2024): 12–20. https://doi.org/10.59324/ejaset.2024.2(1).02.

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Forecasting passenger flow at metro transit stations is a useful method for optimizing the organization of passenger transportation and enhancing operational safety and transportation efficiency. Aiming at the problem that the traditional ARIMA model has poor performance in predicting passenger flow, a hybrid prediction method based on ARIMA-Kalman filtering is proposed. In this regard, ARIMA model training experimental samples are integrated with Kalman filter to create a prediction recursion equation, which is then utilized to estimate passenger flow. The simulation experiment results based

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Lu, Yitong. "Comparative Study of Euro-Dollar Exchange Rate Forecasting Based on BP Neural Network and ARIMA." ITM Web of Conferences 73 (2025): 02018. https://doi.org/10.1051/itmconf/20257302018.

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Effective economic activity prediction is crucial for financial market stability, as accurate exchange rate forecasting can significantly impact international trade and investment decisions. This study aims to anticipate the EUR/USD exchange rate utilizing the automatically picked BP neural network structure with the Autoregressive Integrated Moving Average(ARIMA) model, assessing their efficacy in capturing market dynamics. Traditional models, like ARIMA, often struggle to account for the complexities and nonlinearities of financial markets, which are influenced by various economic and politi

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Xia, Yunya. "Predicting China's Crude Oil Futures Prices: A Strategic Comparison of Random Forest and Time Series Models." Advances in Economics, Management and Political Sciences 136, no. 1 (2024): 114–23. https://doi.org/10.54254/2754-1169/2024.18817.

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This article examines the critical roles of Random Forest (RF) and Time Series (TS) models in forecasting China's crude oil futures prices, providing a comprehensive comparison of their predictive capabilities. The study employs ARIMA and SARIMA models, known for their proficiency in capturing data trends and seasonality, to harness the temporal aspects of oil price movements. In contrast, the RF model is recognized for its robustness in handling complex datasets, offering a nuanced approach to non-linear relationships and variable interactions. The analysis reveals that the ARIMA (0,1,4) mode

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Mo, Hanlin. "Comparative Analysis of Linear Regression, Polynomial Regression, and ARIMA Model for Short-term Stock Price Forecasting." Advances in Economics, Management and Political Sciences 49, no. 1 (2023): 166–75. http://dx.doi.org/10.54254/2754-1169/49/20230509.

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This research investigates the effectiveness of three prominent stock price prediction methodologies: Linear Regression, Polynomial Regression, and AutoRegressive Integrated Moving Average (ARIMA) model. The study leverages one and a half years of historical data from Apple, Tesla, Amazon, and Nike stocks to predict average prices over the ensuing 14 days. The predictive efficacy of each model is tested against actual data, revealing their respective strengths and limitations. Linear Regression offers an overview of stock trends with limited intricacy, while Polynomial Regression delivers a mo

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Mansour, M. H. Alkhazaleh. "Forecasting Banking Volatility in Amman Stock Exchange by Using ARIMA Model." British Journal of Management 29, no. 03 (2018): 09. https://doi.org/10.5281/zenodo.1186692.

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<em>Different techniques of forecasting can be employed in financial markets. This paper introduces one of these techniques which is known as Box-Jenkins model, in which it can be employed for analyzing a financial time series data. The major target of this study is to predict the banking sector volatility in Amman Stock Exchange as an emerging market. This can be achieved by finding the tentative ARIMA model that has the ability to forecast the volatility behavior of the banking sector</em>. <em>The data are obtained from the website of “Amman Stock Exchange” (ASE) on a weekly bas

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