Intelligent Methods for Predicting Financial Time Series

Sustainable tourism, unlike conventional tourism, explicitly strives to maintain economic, environmental, and social sustainability in tourist destinations. Saipan, the capital and the largest island of the chain of Pacific islands known as the Commonwealth of the Northern Mariana Islands (CNMI), is a tropical destination that heavily relies on the tourist industry as a source of income. When analyzing the trends of tourist arrivals from the top three countries that account for the majority of tourist arrivals in Saipan-China, Japan, and Korea-the necessity to implement sustainable tourism in Saipan becomes clear. This article forecasts tourism demand, which is one of the main factors in determining sustainable tourism on a small island. Analyzing both long-term and cyclical trends in tourist arrivals assists budget-constrained policymakers and related organizations in implementing sustainable tourism. Therefore, forecasting accuracy is essential in tourism policy and planning. The tourist arrival data obtained from Marianas Visitors Authority’s official Japanese website are made into a time series for Chinese, Japanese, and Korean visitor data from 2006 to 2016. Given the various time-series forecasting models, we employ simple but powerful forecasting techniques, such as ARIMA and ETS to control for the possible seasonality and trend in our tourism data. Using these forecast methods, the tourist arrival data is used to predict the future trends of tourist arrivals in Saipan for the next twelve months. The performance of our forecasting model in this study is evaluated based on the five measures of accuracy, as measured by comparison with actual tourism flows: mean error, mean absolute scaled error, mean percentage error, mean absolute error, and mean absolute percentage error. A comparison of the two popular forecast techniques in this study shows that the ETS model statistically outperforms the ARIMA model in terms of various measures of accuracy. A practical implication of this finding is that the ETS approach should be used when forecasting tourism demand for small islands. We predict the visitor arrivals from China, Japan, and Korea to Saipan by using the better model. The estimated 80% and 95% intervals for the number of tourist arrivals by the ETS model are presented in this study. The upper (lower) bound is the maximum (minimum) value of the estimated tourist arrivals. We find strong evidence that the tourist arrival trend for Korean visitors to Saipan will increase in the short run; although, the Chinese and Japanese tourist arrival trends seems to be stable, except for Japanese tourist arrivals in August. Nonetheless, with such an intense demand for Saipan’s natural resources, sustainable island tourism becomes necessary. Our estimated result is useful to practitioners in the sense that they are able to prepare for carrying capacities and the means of destination management on Saipan. Proper management for maintaining the island environmentally, economically, and socially requires the preservation of indigenous culture, local quality of life, natural resources, and economic stability while accommodating the demands of arriving tourists. It is crucial to preserve the assets of Saipan in order to maintain and improve its economy. Thus, our time series modeling for forecasting the number of visitors becomes necessary to develop overall economic, environmental, and social sustainability in the island. An interesting addition to implementing sustainable tourism in Saipan is the idea of maintaining a “cap” on the number of incoming tourists visiting the island. Perhaps, with more research on Saipan’s environment, specifically an estimate of how much of the island’s resources tourists exhaust and how much the island can withstand the aforementioned, this “cap” could be practical to prevent perpetual damage to Saipan’s biodiversity, resources, culture, and economy. Along with this idea, knowing the minimum number of tourists needed to sustain Saipan’s necessities is valuable.

Data-driven methods are very useful for streamflow forecasting when the underlying physical relationships are not entirely clear. However, obtaining an accurate data-driven model that is sufficiently performant for streamflow forecasting remains often challenging. This study proposes a new data-driven model that combined the variational mode decomposition (VMD) and the prediction models for daily streamflow forecasting. The prediction models include the autoregressive moving average (ARMA), the gradient boosting regression tree (GBRT), the support vector regression (SVR), and the backpropagation neural network (BPNN). The latest decomposition model, the VMD algorithm, was first applied to extract the multiscale features from the entire time series and to decompose them into several subseries, which were predicted after that using forecast models. The ensemble forecast was finally reconstructed by summing. Historical daily streamflow series recorded at the Wushan and Weijiabao hydrologic stations from 1 January 2001 to 31 December 2014 in China were investigated using the proposed VMD-based models. Three quantitative evaluation indexes, including the Nash–Sutcliffe efficiency coefficient (NSE), the root mean square error (RMSE), and the mean absolute error (MAE), were used to evaluate and compare the predicted results of the proposed VMD-based models with two other models such as nondecomposition method (BPNN) and BPNN based on ensemble empirical mode decomposition (EEMD-BPNN). Furthermore, a comparative analysis of the performance of the VMD-BPNN model under different forecast periods (1, 3, 5, and 7 days) was performed. The results evidenced that the proposed VMD-based models could always achieve good performance in the testing stage and had relatively good stability and representativeness. Specifically, the VMD-BPNN model considered both the prediction accuracy and computation efficiency. The results show that the reliability of the forecasting decreased as the foresight period increased. The model performed satisfactorily up to 7-d lead time. The VMD-BPNN model could be applied as a promising, reliable, and robust prediction tool for short-term streamflow forecasting modelling.

Nowadays , nonlinear time series and artificial neural networks (ANN) models are used for forecasting in the field of business, agriculture and soon. Recent studies have shown, ANN have been successfully used for forecasting of financial and agriculture data series The classical methods used for time series prediction like Box-Jenkins or ARIMA assumes that there is a linear relationship between inputs and outputs. ANN have more advantages that can approximate to model both linear and nonlinear structures in time series, they are not able to handling both structures equally well. The autoregressive integrated moving average (ARIMA) model and two ANN models namely, Radial basis function neural networks (RBFNN), and Elman recurrent neural networks (ERNN) methods were applied to Hyderabad airport traffic data. The data obtained for 15 years from 2002–2003 to 2016–2017 about domestic and international passenger of International Airport of Hyderabad, India. In this research paper, we compared the performances of ARIMA, RBFNN and ERNN were based on three measures: mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE). The results showed that RBFNN obtained the smallest MAE, MAPE and RMSE in both the modeling and forecasting processes. The performances of the three models ranked in ascending order were: ARIMA, ERNN and the RBFNN model. Keywords: T ime series, forecasting, artificial neural networks, ARIMA models, radial basis function neural networks, and Elman recurrent neural networks Cite this Article R. Ramakrishna, Berhe Aregay, Tewodros Gebregergs. The Comparison in Time Series Forecasting of Air Traffic Data by Autoregressive Integrated Moving Average Model, Radial Basis Function and Elman Recurrent Neural Networks. Research & Reviews: Journal of Statistics . 2018; 7(3): 75–90p.

New era has begun two decades ago in the most power sectors of the globe. As electricity itself shifted from being a commodity only to a competitive trading market instrument, forecasting of power load and prices started to be another important factor. With the introduction of liberal power markets, independent players like producers, operators, traders became an important factors of liberalized power markets. New era in this secotr also increased competition among companies and countries. Thus, as new players came into scene, electricity became to be a tradable product and similar to another commodities needs to account, forecast plan of electiricity brought new challenges. With its obstacles, new atmosphere of electiricity repormation brought benefits such as lower price to end users and more utilized energy systems among the countries. Different forecasting models have been developed in order to forsee future operations. Moreover, popular modelts from economics, such as game theories, cournot model, Bertrand model, nash equilibrium plays a big role in electricity price forecasting. Simulation modelts are another methods heavily used by producers, operators and power traders in market. Additionally, statistical models, such as moving averages, Root Mean Square Error, Mean Absolute Error, mean Absolute Percentage Error, Theil's inequality coefficient are frequesntly used to determine price movements of electiricity. As its broadly used in several different industries, Time Series models using historical information and adding updated information helps to model future movement of prices. This article discusses the several methods of price forecasting of electricity in liberal power markets as high volatility of the market raises a big risk for all participants. Forecasting models explains time series analysis and briefly discusses about autoregressive, moving average, autoregressive moving average, and seasonal autoregressive moving average models. Moreover, the article illustrates examples from electricity price and load forecasts and their comparisons with actual results from trades in Turkish electricity market.

The prediction of wind energy generation is important to enhance the performance and dependability of renewable energy systems due to the rising demand for wind-generated electricity and advancements in wind energy technology competitiveness. This study leverages advanced machine learning (ML) and some other statistical and deep learning based time series forecasting models to enhance the accuracy of wind energy predictions. This comprehensive analysis includes nine ML models—Linear Regression, Random Forests (RF), Gradient Boosting Machines (GBM), Support Vector Machines (SVM), K-Nearest Neighbors (KNN), AdaBoost, XGBoost, Support Vector Regression (SVR), and Neural Networks—as well as Four time-series forecasting models—ARIMA, Temporal Convolutional Networks (TCNs), Long Short-Term Memory (LSTM) networks and GRU. Each ML model underwent rigorous cross-validation to ensure optimal performance. The assessment criteria utilized here comprised the Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and the R² Score. It was found that among the nine ML models, Random Forests, GBM and KNN consistently provided superior accuracy and robustness, making them the top choices for wind energy prediction whereas the performance of linear regression, SVM and SVR were very poor for the considered dataset. From the experiment, Random Forest, GBM, and KNN showed the best performance with low MSE values of 0.77, 1.95, and 1.51 respectively, while other models had MSEs above 7.5, with AdaBoost reaching 30. Their RMSEs (0.88, 1.40, 1.23) and MAEs (0.093, 0.73, 0.10) also indicate strong predictive accuracy compared to the rest.In this paper, time series forecasting, TCNs, LSTM and GRU networks showed strong capabilities in capturing temporal dependencies and trends within the wind energy data. Visualization techniques were employed to compare model performances comprehensively, providing clear insights into their predictive power. Therefore, this present study offers a robust framework for researchers and practitioners aiming to leverage machine learning and time series forecasting in the realm of renewable energy prediction.

In the rapidly evolving landscape of retail analytics, the accurate prediction of sales figures holds paramount importance for informed decision-making and operational optimization. Leveraging diverse machine learning methodologies, this study aims to enhance the precision of Walmart sales forecasting, utilizing a comprehensive dataset sourced from Kaggle. Exploratory data analysis reveals intricate patterns and temporal dependencies within the data, prompting the adoption of advanced predictive modeling techniques. Through the implementation of linear regression, ensemble methods such as Random Forest, Gradient Boosting Machines (GBM), eXtreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM), this research endeavors to identify the most effective approach for predicting Walmart sales. Comparative analysis of model performance showcases the superiority of advanced machine learning algorithms over traditional linear models. The results indicate that XGBoost emerges as the optimal predictor for sales forecasting, boasting the lowest Mean Absolute Error (MAE) of 1226.471, Root Mean Squared Error (RMSE) of 1700.981, and an exceptionally high R-squared value of 0.9999900, indicating near-perfect predictive accuracy. This model's performance significantly surpasses that of simpler models such as linear regression, which yielded an MAE of 35632.510 and an RMSE of 80153.858. Insights from bias and fairness measurements underscore the effectiveness of advanced models in mitigating bias and delivering equitable predictions across temporal segments. Our analysis revealed varying levels of bias across different models. Linear Regression, Multiple Regression, and GLM exhibited moderate bias, suggesting some systematic errors in predictions. Decision Tree showed slightly higher bias, while Random Forest demonstrated a unique scenario of negative bias, implying systematic underestimation of predictions. However, models like GBM, XGBoost, and LGB displayed biases closer to zero, indicating more accurate predictions with minimal systematic errors. Notably, the XGBoost model demonstrated the lowest bias, with an MAE of -7.548432 (Table 4), reflecting its superior ability to minimize prediction errors across different conditions. Additionally, fairness analysis revealed that XGBoost maintained robust performance in both holiday and non-holiday periods, with an MAE of 84273.385 for holidays and 1757.721 for non-holidays. Insights from the fairness measurements revealed that Linear Regression, Multiple Regression, and GLM showed consistent predictive performance across both subgroups. Meanwhile, Decision Tree performed similarly for holiday predictions but exhibited better accuracy for non-holiday sales, whereas, Random Forest, XGBoost, GBM, and LGB models displayed lower MAE values for the non-holiday subgroup, indicating potential fairness issues in predicting holiday sales. The study also highlights the importance of model selection and the impact of advanced machine learning techniques on achieving high predictive accuracy and fairness. Ensemble methods like Random Forest and GBM also showed strong performance, with Random Forest achieving an MAE of 12238.782 and an RMSE of 19814.965, and GBM achieving an MAE of 10839.822 and an RMSE of 1700.981. This research emphasizes the significance of leveraging sophisticated analytics tools to navigate the complexities of retail operations and drive strategic decision-making. By utilizing advanced machine learning models, retailers can achieve more accurate sales forecasts, ultimately leading to better inventory management and enhanced operational efficiency. The study reaffirms the transformative potential of data-driven approaches in driving business growth and innovation in the retail sector.

Ensemble approach based on bagging, boosting and stacking for short-term prediction in agribusiness time series

The primary objective of this study is to evaluate the accuracy of different forecasting models for monthly wind farm electricity production. This study compares the effectiveness of three forecasting models: Autoregressive Integrated Moving Average (ARIMA), Seasonal ARIMA (SARIMA), and Support Vector Regression (SVR). This study utilizes data from two wind farms located in Poland—‘Gizałki’ and ‘Łęki Dukielskie’—to exclude the possibility of biased results due to specific characteristics of a single farm and to allow for a more comprehensive comparison of the effectiveness of both time series analysis methods. Model parameterization was optimized through a grid search based on the Mean Absolute Percentage Error (MAPE). The performance of the best models was evaluated using Mean Bias Error (MBE), MAPE, Mean Absolute Error (MAE), and R2Score. For the Gizałki farm, the ARIMA model outperformed SARIMA and SVR, while for the Łęki Dukielskie farm, SARIMA proved to be the most accurate, highlighting the importance of optimizing seasonal parameters. The SVR method demonstrated the lowest effectiveness for both datasets. The results indicate that the ARIMA and SARIMA models are effective for forecasting wind farm energy production. However, their performance is influenced by the specificity of the data and seasonal patterns. The study provides an in-depth analysis of the results and offers suggestions for future research, such as extending the data to include multidimensional time series. Our findings have practical implications for enhancing the accuracy of wind farm energy forecasts, which can significantly improve operational efficiency and planning.

A new multistage short-term wind power forecast model using decomposition and artificial intelligence methods

Oilseeds have been the backbone of India’s agricultural economy since long. Oilseed crops play the second most important role in Indian agricultural economy, next to food grains, in terms of area and production. Oilseeds production in India has increased with time, however, the increasing demand for edible oils necessitated the imports in large quantities, leading to a substantial drain of foreign exchange. The need for addressing this deficit motivated a systematic study of the oilseeds economy to formulate appropriate strategies to bridge the demand-supply gap. In this study, an effort is made to forecast oilseeds production by using Autoregressive Integrated Moving Average (ARIMA) model, which is the most widely used model for forecasting time series. One of the main drawbacks of this model is the presumption of linearity. The Group Method of Data Handling (GMDH) model has also been applied for forecasting the oilseeds production because it contains nonlinear patterns. Both ARIMA and GMDH are mathematical models well-known for time series forecasting. The results obtained by the GMDH are compared with the results of ARIMA model. The comparison of modeling results shows that the GMDH model perform better than the ARIMA model in terms of mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE). The experimental results of both models indicate that the GMDH model is a powerful tool to handle the time series data and it provides a promising technique in time series forecasting methods.

Accurate forecasting of commodity prices remains a crucial challenge due to inherent market volatility and regime-dependent behaviour. This study examines the predictive performance of two nonlinear time series models, the Threshold Autoregressive (TAR) model and the Markov Switching Model (MSM), in modeling and forecasting the prices of gold and cocoa. These commodities exhibit complex dynamics characterized by abrupt structural breaks and asymmetric responses to economic shocks, features that are inadequately captured by linear models. The TAR model is employed to detect endogenous threshold effects, while the MSM accounts for unobservable regime shifts through a probabilistic framework. Monthly average prices of International Cocoa (US$ /tonne) and International Gold (US$ /fine ounce) spanning the period from January 2003 to December 2022 (a 20-year window) were subjected to unit root testing, transformation, and differencing to ensure stationarity prior to modeling. The models’ forecasting accuracy was evaluated using Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). Results indicate that both TAR and MSM significantly improve out-of-sample forecasts by capturing both abrupt and smooth nonlinear transitions. Notably, the gold market showed stronger regime-switching dynamics, while cocoa prices exhibited clearer threshold-based behaviour. MSM model outperforms the TAR model in forecasting gold prices, as it records lower values for both MAE and RMSE, indicating higher predictive accuracy. For Cocoa, TAR slightly outperforms MSM in both MAE and RMSE, though the difference is minimal. Thus, both models perform comparably for Cocoa, with a marginal edge for TAR. Model suitability is observed to be commodity-specific. These findings underscore the utility of regime-sensitive models in commodity price forecasting and offer valuable insights for market participants and policy decision-makers operating in volatile economic environments.

This work contributes to Short Term load forecasting methods by investigating the performance of Prophet method and comparing it with that of Seasonality and Trend Analysis with Loess (STL-ETS) method (popular in forecasting time-series for near future) to establish its capability of capturing multiple seasonality, sudden pattern shift, computational ease & efficiency, simplicity and accuracy. The experimental outcomes validate prophet-based forecasting model for practical deployment. Both models are built using five-year electricity demand data of Texas region, USA. Metric parameters selected to evaluate the performance are Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE) and Mean Absolute Error (MAE). Both forecast models are then utilized to predict and compare the day-ahead half-hourly load of last 15 days of actual data, year 2015. Research work is carried out in R Package (R-core Team, 2020) version 1.3.1093.

Motivation: The substantiation of the scientific problem and the practical value of the study are determined by the enhanced cooperation of the BRICS countries, in particular the growing weight of Russia and China on the world stage. The main objective of the study: is to analyze the problems of the development of bilateral trade between China and Russia and to provide a statistically proven forecast for this trade for 2019-2020, as well as to develop recommendations for the improvement of bilateral trade relations. Novelty: The author's statistical model was developed and its testing was presented through confirming the 24-month forecast of bilateral trade between Russia and China. The model involves solving various problems of bilateral trade. Recommendations for improvement of bilateral trade relations are proposed through formation of an investment and innovation model of bilateral trade.Methodology and Methods: The work used the method of forecasting time series, which suggested the use of a model to predict future values based on previously observed values. To evaluate the modern prospects of the trade between the countries, the authors produced a forecast of the goods turnover trend for 2019-2020. The forecast was issued via the software tool Statgraphics Centurion 18. A reasonable model of the 24-month forecast based on the statistical model Random Walk is developed. The adequacy of the proposed forecast model was subjected to statistical tests. To verify the statistical adequacy of the model the relevant tests were done to determine the compliance of the model with the informational criteria ME (Mean Error), MSE (Mean Squared Error), МАЕ (Mean Absolute Error), МАРЕ (Mean Absolute Percentage Error), МРЕ (Mean Percentage Error). However, it should be noted that the forecast was issued in accordance with the trends which had been identified in the preceding periods. Data and empirical analysis: The factors influencing bilateral trade are analyzed, as well as examples of implemented projects of international cooperation between Russia and China are presented. The current dynamics of sales turnover between Russia and China for the period of 2010-2018 with the use of various statistical and analytical methods is studied, and a reasonable model of the 24-month forecast based on the statistical model Random Walk is developed. The adequacy of the proposed forecast model was subjected to statistical tests. The basic hypothesis is suggested for the upward trend based on reference time series. Policy considerations: It can be said with certainty that the level of technological development of BRICS countries will help Russia and China to start building their cooperation in many fields at a completely new level, taking into account their joint experience in overcoming global crises and Western sanctions. International cooperation between Russia and China in the innovation field will help them unite their efforts and achieve significant synergy. Coordination of countries on this issue will help to reduce production costs, cooperation of production, joint research and development, as well as increase bilateral trade turnover.

Accurate and efficient prediction of oilfield productivity is very important for the formulation of development and adjustment plans. Machine learning (ML) productivity prediction model can quickly obtain the productivity of oilfield development. In this paper, an oilfield development productivity prediction model based on five ML algorithms including multivariable linear regression (LR), extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), back propagation (BP) neural network and long short term memory (LSTM) neural network is established. Through the evaluation of model performance indicators (include the root mean square error (RMSE), the coefficient of determination (R2), the mean absolute error (MAE) and mean absolute percentage error (MAPE)), the best performance prediction model is selected. The research results show that the prediction results of LR model are greatly affected by the data of high productivity oil wells, XGBoost model are easily affected by fitting, and BP neural network model is far less effective than other models. Through comprehensive comparison of prediction results, LightGBM model has better stability and generalization performance. The difference between the prediction results of each model is mainly caused by the characteristics of the algorithm and the size of the data sets. At the same time, LSTM can predict the future oil well production based on the oil well time series observation data. The research results of this paper have guiding significance for the selection of productivity prediction model for oilfield development based on data-driven.

In recent times, an infectious disease namely COVID-19 has affected a large number of individuals. Forecasting models have been helpful in predicting the possible number of confirmed cases, deaths, and recovery counts in the future. In this paper, the prediction of COVID-19 cumulative confirmed cases and deaths for India is analyzed based on various statistical models such as (a) time series, (b) machine learning, and (c) ensemble learning. Autoregressive integrated moving average (ARIMA) and Holt-Winters exponential smoothing in time series; support vector regression (SVR) and linear regression (LR) in machine learning (ML) and random forest regression in ensemble learning (EL) have been implemented for predictions. The accuracies of the trained models are evaluated using metrics such as R-squared value, root mean squared error (RMSE), mean squared error (MSE), mean absolute errors (MAE), and mean absolute percentage error (MAPE). The proposed forecasting models can be used to monitor the rise in COVID-19 cases which can thereby be helpful for government officials to make required changes to their system.KeywordsCOVID-19 pandemic predictionTime-series forecastingEnsemble learningMachine learningSupport vector regression