ARIMA Research Articles

Natural Gas Consumption Forecasting Based on Homoheterogeneous Stacking Ensemble Learning

Natural gas consumption is an important indicator of energy utilization and demand, and its scientific and high-accuracy prediction plays a key role in energy policy formulation. With the development of deep neural networks and ensemble learning, a homoheterogeneous stacking ensemble learning method is proposed for natural gas consumption forecasting. Firstly, to obtain the potential data characteristics, a nonlinear concave and convex transformation-based data dimension enhancement method is designed. Then, with the aid of a stacking ensemble learning framework, the multiscale autoregressive integrated moving average (ARIMA) and high-order fuzzy cognitive map (HFCM) methods are chosen as the base learner models, while the meta learner model is constructed via a well-designed deep neural network with long short-term memory (LSTM) cells. Finally, with the natural gas energy consumption data of national and 30 provinces (where the data of Xizang are unavailable) of China from 2000 to 2019, the numerical results show the proposed algorithm has a better forecasting performance in accuracy, robustness to noise, and sensitivity to data variations than the seven compared traditional and ensemble methods, and the corresponding model applicability rate could achieve more than 90%.

A Small Macro Econometrıc Model of Azerbaıjan Economy

In the presented article, the construction of the Macroeconometric model for short- and medium-term forecasting of indicators of socio-economic development in the economy of Azerbaijan has been carried out. The model consists of 64 variables (41 endogenous, 23 exogenous) and 41 equations, built in four blocks (national income account, deflators, balance of payments and public finance). The database of the model is formed in four parts, covering the years 1995-2017 on an annual basis, including national income account, deflators, balance of payments and public finance. The development of the database was carried out together with the State Statistical Committee of the Republic of Azerbaijan (SSC) in full compliance with UN standards. The methodology used includes time series stationarity tests, ARIMA models, vector error correction models and other econometric methods. As a result, a compact macroeconometric model for the economy of Azerbaijan was established. Based on the model, predictions were processed in the short-term scientific research approach and compared with the actual results.

Electricity user behavior analysis and marketing strategy based on internet of things and big data

This paper examines power user behavior and the design of marketing strategies, using a case study of Smart Community A. We explore how advanced analytical models are used to enhance energy efficiency and user services. First, we apply spectral clustering to refine user segmentation and identify distinct electricity consumption patterns among different groups. Then, the Hidden Markov Model (HMM) analyzes user behavior, uncovering shifts in consumption habits and enabling personalized service offerings. Next, the ARIMA model predicts electricity consumption trends, guiding grid scheduling and resource allocation. Based on these analyses, we develop targeted marketing strategies, such as dynamic pricing and energy-saving incentives, which boost user engagement and reduce energy usage. Through an IoT and big data-driven interactive marketing platform, we enhance user experience and foster a culture of energy conservation. Finally, a feedback mechanism ensures continuous improvement and maximizes the effectiveness of the marketing strategies.

Comparison of Triple Exponential Smoothing and ARIMA in Predicting Cryptocurrency Prices

Cryptocurrency has emerged as a prominent digital asset over the past decade, but its high price volatility presents significant challenges for investors. This study evaluates and compares the effectiveness of the Triple Exponential Smoothing (TES) and Autoregressive Integrated Moving Average (ARIMA) methods in forecasting the prices of five major cryptocurrencies: Bitcoin (BTC), Ethereum (ETH), Binance Coin (BNB), Solana (SOL), and Ripple (XRP). TES models trends and seasonality in time series data, while ARIMA captures autoregressive patterns and moving averages. The dataset is split into 80% for training and 20% for testing, with performance evaluated using Mean Absolute Percentage Error (MAPE) and Root Mean Squared Error (RMSE). TES outperforms ARIMA in predicting Bitcoin and Binance Coin, achieving MAPE values of 10.38% and 13.81%, and RMSE values of 3,985.55 and 41.28, respectively. However, ARIMA shows better performance for Ethereum, Solana, and Ripple, with MAPE ranging from 8.78% to 32.84% and RMSE between 0.08 and 204.59. Notably, Ethereum has the lowest MAPE at 8.78%, while Ripple exhibits the smallest RMSE at 0.08. These findings suggest that TES is more suitable for cryptocurrencies with relatively stable price patterns, while ARIMA is better adapted to forecasting highly volatile assets. This research underscores the importance of selecting forecasting models based on the specific characteristics of each cryptocurrency

Comparison of Machine Learning-Based Predictive Models of the Nutrient Loads Delivered from the Mississippi/Atchafalaya River Basin to the Gulf of Mexico

Predicting nutrient loads is essential to understanding and managing one of the environmental issues faced by the northern Gulf of Mexico hypoxic zone, which poses a severe threat to the Gulf’s healthy ecosystem and economy. The development of hypoxia in the Gulf of Mexico is strongly associated with the eutrophication process initiated by excessive nutrient loads. Due to the complexities in the excessive nutrient loads to the Gulf of Mexico, it is challenging to understand and predict the underlying temporal variation of nutrient loads. The study was aimed at identifying an optimal predictive machine learning model to capture and predict nonlinear behavior of the nutrient loads delivered from the Mississippi/Atchafalaya River Basin (MARB) to the Gulf of Mexico. For this purpose, monthly nutrient loads (N and P) in tons were collected from US Geological Survey (USGS) monitoring station 07373420 from 1980 to 2020. Machine learning models—including autoregressive integrated moving average (ARIMA), gaussian process regression (GPR), single-layer multilayer perceptron (MLP), and a long short-term memory (LSTM) with the single hidden layer—were developed to predict the monthly nutrient loads, and model performances were evaluated by standard assessment metrics—Root Mean Square Error (RMSE) and Correlation Coefficient (R). The residuals of predictive models were examined by the Durbin–Watson statistic. The results showed that MLP and LSTM persistently achieved better accuracy in predicting monthly TN and TP loads compared to GPR and ARIMA. In addition, GPR models achieved slightly better test RMSE score than ARIMA models while their correlation coefficients are much lower than ARIMA models. Moreover, MLP performed slightly better than LSTM in predicting monthly TP loads while LSTM slightly outperformed for TN loads. Furthermore, it was found that the optimizer and number of inputs didn’t show effects on the LSTM performance while they exhibited impacts on MLP outcomes. This study explores the capability of machine learning models to accurately predict nonlinearly fluctuating nutrient loads delivered to the Gulf of Mexico. Further efforts focus on improving the accuracy of forecasting using hybrid models which combine several machine learning models with superior predictive performance for nutrient fluxes throughout the MARB.

Hybrid LSTM-GBRT based machine learning technique implementation for electric vehicle sales prediction analysis

ABSTRACT The investigation of machine learning models in Europe and India for predicting future sales of electric cars (EVs) worldwide has been the focus of present research. In this study, Long Short-Term Memory (LSTM), Support Vector Regression (SVR), Autoregressive Integrated Moving Average (ARIMA), Linear Regression, hybrid long- and short-term memory neural network model and gradient boosted regression trees model (LSTM-GBRT) models based machine learning techniques are compared for performance analysis to predict EV sales. Moreover, in this study, a hybrid LSTM-GBRT technique was developed, which forecasts EV sales, particularly for Battery Electric Vehicles (BEVs) and Plug-in Hybrid Electric Vehicles (PHEVs) over the next five years. During the prediction of EV sales, it has been validated that hybrid LSTM-GBRT technique has better predictive power. In addition, the present study attempts to provide estimated sales statistics for the designated locations. The results indicate that EV industry is evolving, with India showing a strong growth rate in BEV sales and establishing itself as a major participant in the global EV scenario by showing a notable increase in its share of worldwide EV sales volume by 2027. Conversely, the performance of EV sales in Europe has been analysed and validated through the proposed LSTM-GBRT model.

COVID-19 and Mortality in the Spinal Cord Injury Population: Examining the Impact of Sex, Mental Health, and Injury Etiology.

The purpose of this study was to investigate the impact of the COVID-19 pandemic on mortality rates in a community-dwelling spinal cord injury (SCI) population in Ontario. Using health administrative databases, monthly mortality rates were evaluated pre-pandemic, during the pandemic, and post-pandemic from March 2014 to May 2024. Data were stratified by sex, injury etiology, and mental health status. Group differences were evaluated using t-tests. Autoregressive integrated moving average (ARIMA) models evaluated the pandemic's impact on mortality rates. A significant increase of 21.4% in mortality rates during the pandemic was found for the SCI cohort. With the exception of the traumatic group, all subgroups also experienced a significant increase in mortality rates (males: 13.9%, females: 31.9%, non-traumatic: 32.3%, mental health diagnoses: 19.6%, and mental health diagnoses: 29.4%). During the pandemic, females had a significantly higher mortality rate than males. The non-traumatic group had higher mortality rates than the traumatic group at all time periods. Individuals with mental health diagnoses had higher mortality rates than those without at the pre-pandemic and pandemic periods. The variation in mortality rates across groups highlights inequitable access to medical care in the SCI population, with further research and interventions needed.

Time Series Analysis of Onion and Potato Prices using Parametric Models

This study presents a detailed analysis of onion and potato price series using parametric models, specifically Auto- regressive Integrated Moving Average (ARIMA) and Generalized Autoregressive Conditional Heteroscedasticity (GARCH). Data from various Indian markets were used to identify trends, correlations, and volatility. The findings indicate significant volatility in onion prices compared to potato prices, with both series demonstrating non- stationarity and non-normality. The ARIMA models were effective in capturing the seasonality and trends in the data, while GARCH models provided insights into the conditional variance.

Developing a Hybrid ARIMA-XGBOOST Model for Analysing Mobile Money Transaction Data in Kenya

This study formulated a hybrid ARIMA-XGBOOST model using the dataset from the Central Bank of Kenya and the objective was to formulate a Hybrid ARIMA-XGBOOST models to capture the patterns and dynamics in Mobile Money Transactions in Kenya. The study was motivated by the gaps that existed from the previous studies which lacked the ability to model both linear and non-linear trends in the data across time period. Since 2007 when Mpesa was invented, the rate of Mobile Money transactions has rose steadily and this required a complex model that will provide a clear trend. To accurately model the dynamics of Mobile Money Transactions in Kenya, this study presented a complex Hybrid model. The model was formulated by combining Autoregressive Integrated Moving Averages (ARIMA) and Extreme Gradient Boosting (XGBOOST). The ARIMA captures the linear trends of the data while the XGBOOST models the non-linear part and trained to model the ARIMA residuals. The model parameters were evaluated using Mean Absolute Error, Root Mean Square Error among others and confirmed to be accurate and reliable. Based on the findings from the ADF coefficient, the stationary condition was met (P-value=0.01), and therefore we proceeded to develop the ARIMA models. Initial diagnoses included model identification and examination of autocorrelation to determine the ARIMA configurations, whereas the Box-Jenkins test confirmed the models' adequacy (P-value=2.220e-16). Based on Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and Mean Percentage Error (MPE), which are all below 1 percent, indicates that the performance of the models had high prediction accuracy.

Forecasting Foreign Direct Investment Inflow to Bangladesh: Using an Autoregressive Integrated Moving Average and a Machine Learning-Based Random Forest Approach

This study focuses on the challenge of accurately forecasting foreign direct investment (FDI) inflows to Bangladesh, which are crucial for the country’s sustainable economic growth. Although Bangladesh has strong potential as an investment destination, recent FDI inflows have sharply declined due to global economic uncertainties and the impact of the COVID-19 pandemic. There is a clear gap in applying advanced forecasting models, particularly the autoregressive integrated moving average (ARIMA) model and machine learning techniques like random forest (RF), to predict FDI inflows in Bangladesh. This study aims to analyze and forecast FDI inflows in Bangladesh by employing a hybrid approach that integrates the ARIMA model and the RF algorithm. This study covers the period from 1986 to 2022. The analysis reveals that net FDI inflow in Bangladesh is integrated into the first order, and the ARIMA (3,1,2) model is identified as the most suitable based on the Akaike Information Criterion (AIC). Diagnostic tests confirm its consistency and appropriateness for forecasting net FDI inflows in the country. This study’s findings indicate a decreasing trend in net FDI inflows over the forecasted period, with an average of USD 1664 million, similar to recent values. The results from the RF model also support these findings, projecting average net FDI values of USD 1588.99 million. To achieve the aims of Vision 2041, which include eradicating extreme poverty and becoming a high-economic nation, an increasing trend of FDI inflow is crucial. The current forecasting trends provide insights into the potential trajectory of FDI inflows in Bangladesh, highlighting the importance of attracting higher FDI to accomplish their economic goals. Additionally, strengthening bilateral investment agreements and leveraging technology transfer through FDI will also be essential for fostering sustainable economic growth.

Application of time series analysis in predicting postpartum depression: integrating data from the hospitalization period and early postpartum weeks.

This study aimed to explore the dynamic changes in postpartum depressive symptoms from the hospitalization period to 4-8 weeks postpartum using time series analysis techniques. By integrating depressive scores from the hospital stay and the early postpartum weeks, we sought to develop a predictive model to enhance early identification and intervention strategies for Postpartum Depression (PPD). A longitudinal design was employed, analyzing Edinburgh Postnatal Depression Scale (EPDS) scores from 1,287 postpartum women during hospitalization and at 4, 6, and 8 weeks postpartum. Descriptive statistics summarized demographic characteristics. Time Series Analysis using the Auto-Regressive Integrated Moving Average (ARIMA) model explored temporal trends and seasonal variations in EPDS scores. Correlation analysis examined the relationships between EPDS scores and demographic characteristics. Model validation was conducted using a separate dataset. EPDS scores significantly increased from the hospitalization period to 4-8 weeks postpartum (p < .001). The ARIMA model revealed seasonal and trend variations, with higher depressive scores in the winter months. The model's fit indices (AIC = 765.47; BIC = 774.58) indicated a good fit. The Moving Average (MA) coefficient was - 0.69 (p < .001), suggesting significant negative impacts from previous periods' errors. Monitoring postpartum depressive symptoms dynamically was crucial, particularly during the 4-8 weeks postpartum. The seasonal trend of higher depressive scores in winter underscored the need for tailored interventions. Further research using longitudinal and multi-center designs was warranted to validate and extend these findings. Our predictive model aimed to enhance early identification and intervention strategies, contributing to better maternal and infant health outcomes.

FORECASTING CBOE OPENING VALUES: AN EFFECTIVE ARIMA APPROACH WITH TRANSFORMATION AND MODEL DIAGNOSTICS

This study investigates the use of an ARIMA model, coupled with Monte Carlo simulation, to forecast the opening value of a Volatility Index (VIX) time series. The data obtained from the Chicago Board Options Exchange (CBOE) for the years 1992–2019 have been transformed into stationary data using a detrend method and first-order difference. The Augmented Dickey-Fuller (ADF) test is used to ensure the data are adequately transformed. The autocorrelation function (ACF) and partial ACF (PACF) are then used to identify series with serial correlation and determine whether an autoregressive (AR) model is appropriate. Significant moving average (MA) lags are also determined for model identification. The Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC) are used to find the best-fit ARIMA model. Among the evaluated ARIMA models, which ranged from a random walk ARIMA(0, 1, 0) to ARIMA(2, 1, 2), the ARIMA(2, 1, 2) model was found to be the most optimal, exhibiting the lowest AIC and BIC values. This model was then used to forecast the opening value for the year 2014, using 2013 data as the real data. The generated ARIMA(2, 1, 2) model demonstrates reasonable alignment with the actual 2014 data, suggesting its potential for forecasting in this context. Additionally, Monte Carlo simulations are employed to assess the model’s robustness by generating a range of potential outcomes, providing a more comprehensive understanding of the uncertainty associated with the forecasts.

Transformations in BPH Surgical Practices: A Longitudinal Study of Trends and Predictions.

The workload for BPH surgery is expected to rise with an increase in life expectancy and a growing population. Surgical modalities for BPH have evolved, shifting towards minimally invasive transurethral procedures. This study aims to investigate the trends in BPH surgical procedures across medical centers in Israel. Data from seven academic medical centers in Israel from 2010 to 2022 were analyzed. Procedures included open prostatectomy, transurethral prostatectomy (TURP), laser enucleation of the prostate (HoLEP), and photovaporization of the prostate (PVP). Statistical analyses were employed, including t-tests, ANOVA, and ARIMA models. Over 12 years, 13,478 BPH procedures were performed. TURP was the most common (72%), followed by open surgery (12%), HoLEP (12%), and PVP (3%). Annual procedures increased by approximately 5% each year, with a cumulative surge of 63% cases by 2022. The average patient's age remained stable. TURP and HoLEP showed continual growth, while open surgery declined. HoLEP exhibited a shorter length of hospital stay compared to other modalities. Predictive models suggest open prostatectomy will cease within two years, while TURP and HoLEP will continue to rise. This study highlights a significant increase in BPH surgical procedures over time, with a notable preference for TURP and HoLEP. The findings emphasize the importance of size-independent techniques like HoLEP for optimal healthcare management in the future.

MC-VMD-CNN-BiLSTM short-term wind power prediction considering rolling error correction

Abstract Wind energy is a clean and renewable source that has the potential to alleviate the global fossil fuel crisis and environmental pollution by generating electricity. However, accurately predicting wind energy output remains challenging due to its inherent uncertainty. To enhance the accuracy of wind power prediction, a short-term wind power forecasting method for power systems, MC-VMD-CNN-BiLSTM, is proposed, which considers error rolling correction. The method begins with feature selection and outlier handling using the quadrature method. Then, wind power data is decomposed into multiple sub-sequences using the Variational Mode Decomposition (VMD) technique to reduce the raw volatility of wind power. Then, a Convolutional Neural Network (CNN) followed by a Bidirectional Long Short-Term Memory (BiLSTM) model is used for wind power prediction. Finally, the proposed method utilizes the Monte Carlo method for rolling error correction by using known errors from previous time frames to correct subsequent predictions. The MC-VMD-CNN-BiLSTM proposed in this paper considering error rolling correction is compared with ELM, SVM, PSO-BP and ARIMA models through an example analysis of the data of a city, and the proposed model in this paper reduces 61.78%, 50.35%, 62.30% and 73.05% in the NRMSE index in the spring as an example, respectively. The results show that the prediction model proposed in this paper has higher prediction accuracy compared with the traditional prediction model.

Global health impacts of high BMI: A 30-Year analysis of trends and disparities across regions and Demographics

ObjectiveThis study explores the global disease burden associated with high Body Mass Index (BMI) from 1990 to 2021, using data from the Global Burden of Disease Study 2021 (GBD 2021). MethodsWe applied Joinpoint regression to assess trends in deaths and Disability-Adjusted Life Years (DALYs) and employed ARIMA models to project future BMI-related burdens. ResultsFrom 1990 to 2021, global deaths linked to high BMI surged by 153.97%, rising from 1.46 million to 3.71 million. DALYs increased by 167.57%, with the highest rises in North Africa, the Middle East, and South Asia. Women, particularly those aged 75 and above, experienced the most significant burden, with a faster rate of increase in disease burden compared to men post-2000. Future projections indicate a continued rise in BMI-related health impacts, particularly in low- and middle-income countries. ConclusionsThe global disease burden attributable to high BMI is increasing rapidly, particularly in low- and middle-income regions. Targeted public health interventions, especially for women and the elderly, are crucial to addressing this growing health challenge.

B-352 Effects of COVID-19 on Breast Cancer Positivity: A Retrospective and Predictive Study

Abstract Background The COVID-19 pandemic has had a significant impact on the healthcare system, resulting in overall reduction in access to healthcare services, including the suspension of screening programs and postponement of surgeries and medical treatments. The aim of this study was to evaluate the effects of the pandemic on breast cancer positivity from cases received at a laboratory in São Paulo, Brazil. Methods A retrospective analysis of a database from a pathology laboratory in São Paulo, Brazil, was performed during the 2017-2023 period and classified as following: pre-pandemic (January 2017-February 2020), pandemic (March 2020 - April-2022), and post-pandemic (May 2022-December 2023). Cases were classified as negative or positive for malignancies. Positive anatomopathological examinations (breast biopsies and surgical specimens) considered primary, metastatic mesenchymal or endothelial malignancies. The Mann-Kendall test was used to evaluate the trend, and the PACF (Partial Autocorrelation Function) correlogram was used to analyze historical memory for definition of the autoregressive model. The augmented Dickey-Fuller hypothesis test was applied to analyze whether the series is stationary or not, and the first differences test was applied. The ARIMA model was used for the estimates (1,1,1). Statistics were performed using the gretl software and pymannkendall library in the Python language. Results A total of 74,973 samples were evaluated during the 2017-2023 period. In the three-year pre-pandemic period, 32,644 tests were recorded, with an average of 29.34% positive cases. From the analysis of the pre-pandemic time series, there was a positive trend towards an increase in cases (p &amp;lt; 0.0001, z=6.1360, Tau=0.6955). In a non-pandemic scenario, from March 2020 to February 2021, the number of new tests expected would be 11,535; however, in the pandemic period, only 8,904 were observed, a 22.81% reduction on expected cases. According to the historical average positivity rate in the pre-pandemic period, the expected and observed total were, respectively, 3,384 and 2,566 positive cases for malignancy. Conclusions This study reveals a significant decrease of approximately 22.81% in new breast anatomopathological diagnoses during the COVID-19 pandemic in a laboratory in São Paulo, Brazil. For comparison purposes, we identified a 24.17% of missed opportunities on diagnoses of new malignancy-positive cases, compared to the pre-pandemic period. These findings highlight the need for strategies to mitigate the negative effects of the pandemic on the early detection and treatment of breast cancer.

Quantification of uncertainty in short-term tropospheric column density risks for a wide range of carbon monoxide

The short-term risks associated with atmospheric trace gases, particularly carbon monoxide (CO), are critical for ecological security and human health. Traditional statistical methods, which still dominate the assessment of these risks, limit the potential for enhanced accuracy and reliability. This study evaluates the performance of traditional models (ARIMA), machine learning models (LightGBM, ConvLSTM2D), and optimized machine learning solutions (Bayes residual optimization ConvLSTM2D LightGBM, Bayes_CL) in predicting Sentinel 5P columnar CO levels. This study findings demonstrate that machine learning models and their optimized versions significantly outperform traditional ARIMA models in cross-validation (CV), visualization, and overall prediction performance. Notably, machine learning model based on Bayes and residual optimization (Bayes_CL) achieved the highest CV score (Bayes_CL R2 = 0.8, LightGBM R2 = 0.79, ConvLSTM2D R2 = 0.75, ARIMA R2 = 0.61), along with superior visualization and other metrics. Using Bayes_CL, we effectively quantified a 2.4% increase in columnar CO levels in mainland China in the second half of 2023, following the complete lifting of COVID-19 lockdowns. This study confirms that machine learning models can effectively replace traditional methods for short-term risk assessment of Sentinel 5P columnar CO. This transition holds significant implications for policy formulation, greenhouse effect assessment, and population health risk evaluation, especially in uncertain situations where human activities are severely disrupted, thereby affecting environmental safety.

Hybrid Modeling Techniques for Municipal Solid Waste Forecasting: An Application to OECD Countries

Abstract Accurate forecasting of municipal solid waste (MSW) generation is critical for effective waste management, given the rising volumes of waste posing environmental and public health challenges. This study investigates the efficacy of hybrid forecasting models in predicting MSW generation trends across Organization for Economic Cooperation and Development (OECD) countries. The empirical analysis utilizes five distinct approaches – ARIMA, Theta model, neural networks, exponential smoothing state space (ETS), and TBATS models. MSW data spanning 1995–2021 for 29 OECD nations are analyzed using the hybrid models and benchmarked against individual ARIMA models. The results demonstrate superior predictive accuracy for the hybrid models across multiple error metrics, capturing complex data patterns and relationships missed by individual models. The forecasts project continued MSW generation growth in most countries but reveal nuanced country-level differences as well. The implications for waste management policies include implementing waste reduction and recycling programs, investing in infrastructure and technology, enhancing public education, implementing pricing incentives, rigorous monitoring and evaluation of practices, and multi-stakeholder collaboration. However, uncertainties related to model selection and data limitations warrant acknowledgment. Overall, this study affirms the value of hybrid forecasting models in providing robust insights to inform evidence-based waste management strategies and transition toward sustainability in the OECD region.

Evaluation of Trend Models Performance and Forecasting Onion Production: A Comparative Study

Onions, a significant spice crop in Bangladesh, frequently experience price surges due to an imbalance between demand and supply, compelling the government to import onions. The volatility of the onion market in Bangladesh and the measures needed to stabilize the situation require intensive policy attention. Therefore, this study was carried out to forecast the onion production over time and to evaluate the efficacy of various trend models. The secondary data was collected from the Food and Agriculture Organization's Statistical Database (FAOSTAT) from 1972 to 2022. The autoregressive integrated moving average (ARIMA) model was used to predict the onion production over time. Additionally, metrics such as modified and the coefficient of determination were used to evaluate the effectiveness of eight trend models comprehensively. The findings indicate that the cubic model is superior to others since it has larger and adjusted values. The models' accuracy is also determined utilizing mean absolute percentage error (MAPE) and mean absolute deviation (MAD) metrics. The cubic trend model exhibits lower MAPE and MAD values of 29.43% and 80638.67, respectively. Therefore, it is concluded that the ARIMA (2, 2, 2) model is the best model for forecasting the annual production of onion in Bangladesh. A five-year trend analysis spanning from 2023-2027 indicates an upward trend. Such a result offers a critical insight for stakeholders and policymakers in formulating sustainable onion production strategies. J Bangladesh Agril Univ 22(3): 386-395, 2024

Development of multi-forecasting model using Monte Carlo simulation coupled with wavelet denoising-ARIMA model

The analysis and prediction of stock market prices are crucial areas of research due to their complex, chaotic, and nonlinear features. As a result, making significant gains in stock market investments is a crucial task. However, expert and intelligent modeling techniques can help in achieving positive stock market returns. In this study, we use the Monte Carlo (MC) simulation method to generate multiple future values of the time series of closing prices of a particular stock of BSE using a combination of wavelet denoising and the autoregressive integrated moving average (ARIMA) model. The multiple future realizations of stock prices produced by the Monte Carlo (MC) simulation can help minimize risk and uncertainty in stock market investments. Firstly, we use wavelet analysis to detect significant noise levels in the time series at each scale in discrete wavelet decomposition, which is then eliminated by an appropriate wavelet denoising method. Next, the time series of denoised stock prices is fitted with a suitable ARIMA model, and the future values are obtained using this model. The future values of the denoised time series are simulated using MC simulation. The results of the study show that simulated forecasts obtained by MC simulation using the integrated wavelet-denoising-ARIMA model become more accurate with increasing simulation count than by applying a single ARIMA model to noisy stock price series. It has also been observed that MC simulation reduces the standard error of estimates to one half when the number of simulations is quadrupled.