ARIMA Forecasts Research Articles

Assessing the Profit Impact of ARIMA and Neural Network Demand Forecasts in Retail Inventory Replenishment

This study investigates the integration of demand forecasting and inventory replenishment strategies to enhance retail profitability. A deterministic optimal replenishment model is utilized to analyze the predictive performance of various neural network architectures and ARIMA models using real sales data. The predictive accuracy and subsequent influence on optimal firm profits over a multi-period planning horizon is assessed. The Integer Programming model devised optimizes daily replenishment across multiple retail routes, taking into account sales revenue, supply costs, inventory holding, sales loss, and transportation expenses. The study is distinctive in its dual assessment: it evaluates both the accuracy of forecasting methods and their direct impact on profitability through systematic inventory decisions. Neural network architectures selected for minimizing error in product sales predictions have 6% lower mean squared error compared to Akaike Information Criterion minimizing ARIMA models. For longer horizon predictions necessary in performance gap grows larger, e.g., with %60 difference for predictions 15 days ahead. Predictions reflect as 1.6% higher profits on average, when neural network predictions and more efficient longer planning horizons of the optimization model are preferred. Planning 30 days ahead, optimizing with neural network predictions elicits 2.3% higher profits compared to profits attainable based on ARIMA predictions. Our findings illustrate how different forecasting methods can affect firm profitability by shaping inventory replenishment strategies. By merging mathematical optimization with time series forecasting, this research provides a comprehensive evaluation of how advanced predictive technologies can enhance retail inventory practices and improve profitability.

A Study on Energy Production and Consumption Based on Seasonal ARIMA and REAO

In the face of the global energy challenge, this study delves into the intricate landscape of energy dynamics within the rapidly evolving global economy and technological sphere. Recognizing energy as a pivotal driver of societal progress, we present a comprehensive evaluation and optimization model for China's energy sources, emphasizing economic, cost, electric energy, and environmental considerations. Employing time series models, specifically ARIMA and gray scale forecasting, we meticulously analyze wind and solar power generation, as well as overall power consumption. The results showcase nuclear power and hydropower as significant contributors to the energy landscape. Leveraging a linear programming model, we simulate and optimize the future energy development structure, considering constraints such as cost, energy, capacity, and carbon emissions. Our findings reveal the optimal quota contributions of thermal power, hydro power, nuclear power, wind power, and solar power as 13.7%, 14.2%, 17.6%, 54.5%, and 0%. Building upon these insights, we propose strategic recommendations for sustainable electric energy development. Emphasizing wind and solar energy's potential, we advocate for continued interest and increased support for technological advancements. Additionally, we call for the establishment of comprehensive power and energy development plans, integrating economic and environmental goals. To address imperfections in China's electricity tariff system, we recommend ongoing reforms aligned with market dynamics and environmental considerations.

Research on Nanyang City's Industrial Development Based on Statistical Analysis and ARIMA Forecasting

In recent years, Nanyang City has focused on balancing rapid industrial growth with sustainable development by integrating traditional industries with emerging high-tech sectors to enhance competitiveness and reduce environmental impacts. This study examines the industrial development of Nanyang City using statistical analysis and ARIMA forecasting. Key indicators such as GDP, number of enterprises, employees, and industrial pollution were sourced from the Nanyang Statistics Bureau, preprocessed for quality and consistency, and normalized. The ARIMA model, following stationarity tests and optimal parameter identification, predicts a slight GDP decline in 2023 and, a recovery in 2024, but slower growth by 2027. Environmental indicators were evaluated using Pearson correlation coefficients and the Analytic Hierarchy Process, revealing volatility in green development. The study suggests that Nanyang should strengthen industrial chain integration and technological innovation to enhance competitiveness and environmental performance.

Exploring the Impact of Routine Activity and Financial Strain on Fraud Victimization During the COVID-19 Pandemic in Hong Kong

This study examines the impact of the COVID-19 pandemic on fraud victimization in Hong Kong, providing valuable insights beyond the Western context. Drawing on general strain theory and routine activity theory, this research explores the influence of economic recession at the local and adjacent societal levels, as well as residential duration (refers to relative time spent at residences), on fraud victimization in Hong Kong. Utilizing 10 years (120 months) of monthly police-recorded victimization data, this study employs various methodologies, including ARIMA forecasting, single-group interrupted time series analysis (ITSA), and Poisson regression, to explore the impact of the COVID-19 pandemic on fraud victimization in Hong Kong. The ARIMA framework reveals an unexpected and significant increase in fraud victimization during the COVID-19 period, surpassing the predicted levels. The ITSA results demonstrate that the pandemic had a short-term and long-term effect on fraud victimization in Hong Kong. To further understand the factors contributing to this change, a Poisson regression analysis is conducted. The findings highlight the positive and significant impact of residential duration and the unemployment rate in mainland China on fraud victimization, aligning with the propositions of routine activity theory and general strain theory. Limitations and policy implications at both the local and international levels are discussed.

Assimilation of PSO and SVR into an improved ARIMA model for monthly precipitation forecasting

Precipitation due to its complex nature requires a comprehensive model for forecasting purposes and the efficiency of improved ARIMA (IARIMA) forecasts has been proved relative to the conventional models. This study used two procedures in the structure of IARIMA to obtain accurate monthly precipitation forecasts in four stations located in northern Iran; Bandar Anzali, Rasht, Ramsar, and Babolsar. The first procedure applied support vector regression (SVR) for modeling the statistical characteristics and monthly precipitation of each class, IARIMA-SVR, which improved the evaluation metrics so that the decrease of Theil's coefficient and average relative variance in all stations was 21.14% and 17.06%, respectively. Two approaches are defined in the second procedure which includes a forecast combination (C) scheme, IARIMA-C-particle swarm optimization (PSO), and artificial intelligence technique. Generally, most of the time, IARIMA-C-PSO relative to the other approach, exhibited acceptable results and the accuracy improvement was greater than zero at all stations. Comparing the two procedures, it is found that the capability of IARIMA-C-PSO is higher concerning the IARIMA-SVR, so the decrease in the normalized mean squared error value from IARIMA to IARIMA-SVR and IARIMA-C-PSO is 36.72% and 39.92%, respectively for all stations. The residual predictive deviation (RPD) of IARIMA-C-PSO for all stations is greater than 2, which indicates the high performance of the model. With a comprehensive investigation, the performance of Bandar Anzali station is better than the other stations. By developing an improved ARIMA model, one can achieve a high performance in structure identifying and forecasting of monthly time series which is one of the issues of interest and importance.

Predictions from Generative Artificial Intelligence Models: Towards a New Benchmark in Forecasting Practice

This paper aims to determine whether there is a case for promoting a new benchmark for forecasting practice via the innovative application of generative artificial intelligence (Gen-AI) for predicting the future. Today, forecasts can be generated via Gen-AI models without the need for an in-depth understanding of forecasting theory, practice, or coding. Therefore, using three datasets, we present a comparative analysis of forecasts from Gen-AI models against forecasts from seven univariate and automated models from the forecast package in R, covering both parametric and non-parametric forecasting techniques. In some cases, we find statistically significant evidence to conclude that forecasts from Gen-AI models can outperform forecasts from popular benchmarks like seasonal ARIMA, seasonal naïve, exponential smoothing, and Theta forecasts (to name a few). Our findings also indicate that the accuracy of forecasts from Gen-AI models can vary not only based on the underlying data structure but also on the quality of prompt engineering (thus highlighting the continued importance of forecasting education), with the forecast accuracy appearing to improve at longer horizons. Therefore, we find some evidence towards promoting forecasts from Gen-AI models as benchmarks in future forecasting practice. However, at present, users are cautioned against reliability issues and Gen-AI being a black box in some cases.

The future of regional inequalities: an ARIMA forecast

The future of regional inequalities: an ARIMA forecast

Construction Price Index Prediction through ARMA with Inflation Effect: Case of Thailand Construction Industry

Over-budgeting due to inflation is a common phenomenon in the construction industry of both developed and developing countries. Inflation, with time changes, leaves an adverse effect on the project budget. Hence, this study aims to focus on the construction price index (CPI) behavior and inspect its correlation with inflation in Thailand’s construction industry as there has not been much work performed. The prediction of CPI was made from 2024 to 2028, relying on the data set from 2000 to 2023. The relationship between inflation and CPI categories helps in prediction by considering inflation as the independent variable and CPI (All Commodities, Lumber and Wood Products, Cement, and Iron Products) as the dependent variable that was incorporated in EViews to perform automated ARIMA forecasting. The correlation results show that out of four CPI, only Iron Products showed a significant relationship with inflation. For All Commodities, Lumber, and Wood Products, the predicted values were fluctuating, while for Cement and Iron Products, a clear seasonal pattern was observed. This prediction gives a direction to construction industry practitioners to make necessary adjustments to their budget estimation before signing the contract to overcome cost overrun obstruction.

Improving the accuracy of credit scoring models using an innovative Bayesian informative prior specification method

A new Bayesian informative prior specification method (BAF method – Bayesian priors using ARIMA forecasts) is proposed to introduce additional information into credit risk modelling and improve model predictive performance. We use logistic regression to model the probability of default of mortgage loans comparing the Bayesian approach with various priors and the frequentist approach. But unlike previous literature, we treat coefficient estimates in the probability of default model as stochastic time series variables. We build ARIMA models to forecast the coefficient values in future time periods and use these ARIMA forecasts as Bayesian informative priors. We find that the Bayesian models using this prior specification method produce more accurate predictions for the probability of default as compared to frequentist models and Bayesian models with other priors.

2019–2023 in Review: Projecting DDoS Threats With ARIMA and ETS Forecasting Techniques

2019–2023 in Review: Projecting DDoS Threats With ARIMA and ETS Forecasting Techniques

Key Drivers of Economic Growth in Portugal and the Eurozone

This paper examines the influence of gross household disposable income, tax burden, and savings rates on economic growth in Portugal, providing a comparative analysis with the Euro Zone. Drawing on empirical data from 2000 to 2022, the study employs Pearson correlations, regression models, and ARIMA forecasts to analyse the relationships among these variables. The findings indicate a significant positive correlation between disposable income and economic growth, while Portugal's higher tax burden has a more pronounced negative impact on growth than the Euro Zone. Additionally, the research highlights the critical role of savings rates in driving economic growth, particularly in Portugal, where lower savings rates are partly attributable to the financial system's development. The results suggest that policies aimed at increasing disposable income, reducing the tax burden, and fostering savings could substantially boost economic growth in Portugal, offering insights into the broader determinants of growth in developed economies.

Enhancing Sustainable Dairy Industry Growth through Cold-Supply-Chain-Integrated Production Forecasting

Cold supply chains (CSCs) are critical for preserving the quality and safety of perishable products like milk, which plays a vital role in the daily lives of a vast population, especially in countries like India. This research centers on sustainable milk production in Northern India, with priorities of ensuring efficiency and waste reduction within the cold supply chain. Leveraging data from a prominent North India-based dairy company, Company ‘X’, an ARIMA model is applied for predicting monthly milk production trends. Utilizing the Statistical Package for the Social Sciences (IBM SPSS STATISTICS 20) software, the study forecasts Company ‘X’s monthly milk production and identifies four distinct ARIMA models based on the autocorrelation function (ACF) and the partial autocorrelation function (PACF). By comparing predicted and actual milk production values (April–October 2021), sustainability metrics are integrated into ARIMA forecasts. Implications for the dairy sector’s sustainability and alignment with the Sustainable Development Goals (SDGs) are assessed through error terms such as R squared (R2) and mean absolute percentage error (MAPE). The study promotes sustainable milk production practices in Northern India’s dairy sector, resonating with the SDGs to optimize demand–supply dynamics and foster a more environmentally conscious dairy industry.

Forecasting GDP of Nepal using Autoregressive Integrated Moving Average (ARIMA) Model

Background: Globally many research are working on modeling and forecasting of gross domestic product (GDP). The trend and pattern will help the planner and policy maker to make future monetary policy. The aim of this research is to find the ARIMA model and forecasting.
 Methods: Box-Jenkins methodology was use for the modeling and forecasting of annual GDP series of Nepal from 1990/91 to 2019/20. Eviews 10 software was use for data analysis.
 Results: Using the Box-Jenkins methodology this research examine the number of ARIMA family model that describe the annual GDP series and the appropriate model is ARIMA(1,1,1).
 Conclusions: This research concluded that ARIMA(1,1,1) is the model which capture the GDP series of Nepal for this period.

Managing the EU energy crisis and greenhouse gas emissions: Seasonal ARIMA forecast

Changes in the logistics of energy resources and their potential shortage are causing a review of the EU energy policy. The energy sector significantly affects the progress toward achieving climate policy goals due to significt greenhouse gas emissions. The REPowerEU plan, implemented in the EU27 to overcome the energy crisis, requires new forecasts of greenhouse gas emissions due to a change in European energy policy. This paper aims to examine the consequences of the management of the energy crisis caused by Russia’s invasion of Ukraine on EU climate policy. This study focuses on forecasting greenhouse gas emissions in the EU until 2030 and uses the Seasonal ARIMA model based on quarterly time series in the EU27. Depending on energy management and changes in energy policy to overcome the energy crisis, a positive or negative scenario for greenhouse gas emissions may occur. An important parameter that should be considered when determining the scenario of the EU energy development according to climate policy was defined by correlation analysis.According to the negative scenario and under the influence of the effects of the Russian invasion of Ukraine, the value of greenhouse gas emissions in the EU at the beginning of 2030 will be 0.752911 tons per capita. The positive scenario shows greenhouse gas emissions can be reduced to 0.235225 tons per capita.The study results proved two extreme scenarios of greenhouse gas emissions, depending on how to overcome the energy crisis. AcknowledgmentThe authors appreciate the copyright holder: © European Union, 1995–2022, as well as the source of the extracted data, which is the European Commission website, Eurostat http://ec.europa.eu/eurostat (accessed on 16 October 2022). This study was funded by the European Union (the project No. 101048079 – EU4SmartED – ERASMUS-JMO-2021-HEI-TCH-RSCH); by the Ministry of Education and Science of Ukraine (projects No. 0122U000788, 0122U000769, 0121U109553, 0120U102001, 0122U000777).This research was funded by Faculty of Organization and Management of the Silesian University of Technology (grant number: 13/990/BK_23/0178).

Application of ARIMA Model and Exponential Smoothing Method in GDP Prediction of the United States

The GDP of the United States always attracts the global attention. It represents the strength of the world's largest economy and the per capita living standard of developed countries. Therefore, many scholars try to use various methods to predict it. On the basis of previous research, this paper collects the Fred’s quarterly U.S. GDP data, uses ARIMA model and exponential smoothing to model the US's GDP from 1950 to 2021. Then, the authors compare the actual values with the predicted values to select a better model. After that, the established model is used to forecast the US's GDP. The results show that the two methods generally maintain a high degree of fit, but the error generated increases with the widening of the expected range. Among these two methods, the average error value of the exponential smoothing method is slightly smaller than ARIMA, but ARIMA's forecasts are more accurate in the early. Therefore, ARIMA should be used preferentially for the forecast in the short term and the exponential smoothing method should be preferred for long-term forecasts. Furthermore, we use the exponential smoothing method to make a short-term forecast for the U.S. GDP by the fourth quarter of 2023. The results show that the U.S. GDP will increase at an average rate of 0.9344%, and the upward trend is stable and cyclical. Finally, the result of the above provides an effective basis for the US economic construction work and plays an inspirational role for China's economic policy formulation.

Analysis of Air Pollution (SO2) at Some Point of Congestion in DKI Jakarta

Background: Air pollution is the entry or inclusion of energy substances from other components into the air by humans so that air quality drops to a certain level which causes the air to not meet the requirements. This study aims to determine concentration levels of particulates (PM10, PM25), sulfur dioxide (SO2), carbon monoxide (CO), ozone (O3), and nitrogen dioxide (NO2) at several congestion points in Daerah Khusus Ibukota (DKI) Jakarta. Method: The research location based on the purposive sampling method was carried out at 5 congestion points in DKI Jakarta namely Bundaran Hotel Indonesia (HI), Kelapa Gading, Jagakarsa, Lubang Buaya, and Kebon Jeruk. The data used in the process of forming the model using the Box-Jenkins method and analyzed manually, so this process can be done using ARIMA Forecasting. Results: Based on the time series of hourly data, which was calculated on average into daily data, the concentration of PM10, PM25, SO2, CO, O3, and NO2 from January to October 2021 has decreased from the previous months at several congestion points in DKI. Conclusion: The conclusion of this study is the decreased concentration of PM10, PM25, SO2, CO, O3, and NO2 at several points in DKI Jakarta, so it is recommended to the Government and the Community make efforts/activities to maintain the healthy air quality in DKI Jakarta.

COMPARATIVE PERFORMANCE ANALYSIS OF ARIMA, PROPHET AND HOLT-WINTERS FORECASTING METHODS ON EUROPEAN COVID-19 DATA

COVID-19 is the most common infectious disease of the last few years and has caused an outbreak all around the world. The mortality rate, which was earlier in the hundreds, increased to thousands and then to millions. Since January 2020, several scientists attempted to understand and predict the spread of COVID-19 so that governments may make sufficient arrangements in hospitals and reduce the mortality rate. This research article presents a comparative performance analysis of ARIMA, Prophet and Holt-Winters Exponential Smoothing forecasting methods to make predictions for the COVID-19 disease epidemiology in Europe. The dataset has been collected from the World Health Organization (WHO) and includes the COVID-19 case data of European countries, which is categorized by WHO between the years of 2020 and 2022. The results indicate that Holt-Winters Exponential Smoothing method (RMSE: 0.2080, MAE: 0.1747) outperforms ARIMA and Prophet forecasting methods.

Human development at the cost of the environment?-an application of planetary pressures-adjusted human development index in the lens of planetary boundaries.

The human development index (HDI) was an important step toward a more human-oriented assessment of socioeconomic prosperity. Nevertheless, at the onset of the anthropocene, the environmental pillar of sustainable development is indispensable. This work aims to understand this conundrum of human development and its environmental cost or pressure through the use of the planetary pressures-adjusted human development index (PHDI) as well as introduce another modified version, planetary pressures, and inequality-adjusted human development index (PIHDI). PHDI incorporates two biophysical consumption-based indicators (CO2 emissions and material footprint, MF) as a proxy of environmental pressures into traditionally socioeconomic development-focused HDI. This work spans 164 nations and 27years (1990-2016). Various statistical techniques such as Pearson's correlation, hierarchical clustering (HCA), generalised additive modelling (GAM), data envelopment analysis (DEA), linear regression, and ARIMA forecasting have been used to explore interrelationships, non-linearity, efficiency analysis, and future projections (up to 2030) and delve into two scenarios: high human development and for human development permitted only within the two planetary boundaries (PBs) (viz. climate change and material footprint) and their consequences when exceeded. Though most of the countries with high PHDI and PIHDI scores are from the global north and have a high income, it is also possible to attain human development (i.e. increase PHDI and PIHDI scores) without overexploiting biophysical resources. From 2016, human development scores could increase by 55-63% (climate change) or 30-46% (material consumption) within a safe operating space in 2030. Without the required focus on the environment, aiming for a superior score in PHDI and PIHDI could result in 43-58% (CO2 emissions) or 57-58% (material footprint) of countries that would exceed PB. Based on the outcome of this work, it can be recommended that governments and policymakers that it is well within the limits of feasibility as well as necessary to make human socioeconomic development aspire to sustainability to address the needs of humanity, while respecting the well-being of the surrounding biosphere.

Stock Price Prediction Using Arima Forecasting and LSTM Based Forecasting, Competitive Analysis

Abstract: Given the commercial and personnel assets involved as well as the unpredictable nature of the gains switching limbs, stock systems are among absolute highly exciting areas for net worth progress and GDP expansion. Forecasting the future and performances of every stock industry could help investors accumulate wealth during prosperous periods and reduce liabilities during turbulent moments. "Stock industry forecasting" is the process of estimating the eventual worth of transfer business shares and comparable monetary assets. Stock price forecasting has long been a popular area of study. Nevertheless, the widely adopted auto - regressive integrative movement averaged (ARIMA) approach has its inherent benefits and drawbacks. Longer short-term memory (LSTM) systems paradigm consumption towards forecasting additionally demonstrates intriguing potential. By comparing the concepts of such different approaches and the outcomes of predictions, this paper particularly contrasts such two concepts. The LSTM framework is thought to have the strongest forecasting power inside the end; however, data manipulation has a significant impact on it.

SECTORAL MARKET RISK PREMIUMS IN TURKEY

Purpose- This empirical study aims to measure the sectoral market risk premiums in the Turkish stock market for the period of 2016 and 2021 and also estimate the sectoral market risk premiums for the years 2022, 2023 and 2024. Capital Asset Pricing Model (CAPM) is the most widely used and popular method in the analysis of investment projects, stock valuation, firm valuation, mergers and acquisitions, initial public offerings, and secondary public offerings. The market risk premium in CAPM is defined as the the difference in between expected market returns and interest rates. The determination of market risk premium is one of the most important inputs in the application of the CAPM. This study intends to calculate the market risk premiums and volatilities for the sectors of Borsa Istanbul for the periods of pre-Covid (2016-2017-2018) and in the Covid-19 era (2019-2020-2021). Methodology- The monthly data from the Reuters Database are collected for the BIST100 and 17 different sectoral indexes and short-term interest rates between the years 2016 and 2021. A total of 1296 observations are obtained. Based upon the historical observations, the market risk premiums are defined as the difference between the market index returns (BIST100 and 17 sectoral indexes) and the average short-term interest rates on monthly basis. Then, using the ARIMA forecasting method, the market risk premiums are estimated for the years 2022, 2023, and 2024. A total of 576 data points are forecasted. Findings- The average risk premium on the BIST100 index is about -2.44% for the pre-Covid era and 14.01% for in-Covid era. The market risk premiums sharply increased from the pre-Covid period to the Covid period. The average volatility on the BIST100 index is about 0.23% for the pre-Covid era while 0.34% in the Covid era. The volatility of the market returns also incresed significantly. Moreover, the Cusum Square Test results point a structural break in the Covid-era. The ARIMA estimates of market risk premiums are 1.87% for 2022, 0.43% for 2023 and 0.42% for 2024. THe ARIMA estimates of volatilities are 0.70% for 2022, 0.72% for 2023 and 0.71% for 2024. Conclusion- The empirical evidence strongly support a structural change in the Covid era with higher market risk premiums and volatilities. The forecasted market risk premiums for the next three years show a diminishing trend while the forecasted volatilities show high and persistent level. Keywords: Market risk premium, BIST100, ARIMA forecasting, sectoral market risk premiums, volatility. JEL Codes: G10, G12, G17