Search Results (12)

This paper proposes hybrid LSTM-DES models that combine traditional forecasting methods with recurrent neural network techniques. We experimented with these proposed models using four passenger datasets from different regions of Thailand. Additionally, we compared their performance with several individual forecasting models, including the Double Moving Average (DMA), Double Exponential Smoothing (DES), and Holt–Winters methods (both additive and multiplicative trends), as well as long short-term memory (LSTM) recurrent neural networks. Our proposed hybrid model builds upon previous work with improvements in hyperparameter tuning using the GRG nonlinear optimization method. The results demonstrate that the hybrid LSTM-DES models outperformed all individual models in terms of both accuracy and demand variation. The reason behind the success of the hybrid model is that it works well with both linear and nonlinear trends, as well as the seasonality of certain periods. Furthermore, the forecast results for train passengers will serve as input variables to estimate the future revenue of train travel programs in various regions, including rail tourism. This information will help identify which regions should receive increased focus and investment by the train tourism program. For example, if the forecasted number of passengers in the northern region is high, the State Railway of Thailand will promote and improve infrastructure at the train station and nearby tourist attractions. Full article

Time series forecasting plays a critical role in business planning by offering insights for a competitive advantage. This study compared three forecasting methods: the Holt–Winters, Bagging Holt–Winters, and Box–Jenkins methods. Ten datasets exhibiting linear and non-linear trends and clear and ambiguous seasonal patterns were selected for analysis. The Holt–Winters method was tested using seven initial configurations, while the Bagging Holt–Winters and Box–Jenkins methods were also evaluated. The model performance was assessed using the Root-Mean-Square Error (RMSE) to identify the most effective model, with the Mean Absolute Percentage Error (MAPE) used to gauge the accuracy. Findings indicate that the Bagging Holt–Winters method consistently outperformed the other methods across all the datasets. It effectively handles linear and non-linear trends and clear and ambiguous seasonal patterns. Moreover, the seventh initial configurationdelivered the most accurate forecasts for the Holt–Winters method and is recommended as the optimal starting point. Full article

Corporate sustainability has garnered increasing attention within the business community as corporations communicate to influence their stakeholders to build sustainable relationships. There has been a surge in research exploring its connection to firm performance, but existing studies lack a cohesive and concentrated approach. The aim of this study is to explore the trends of growth of publications; gauge the annual growth rate, annual ratio of growth, relative growth rate, doubling time, and scientific production index; predict future production levels; and look at the relationship between corporate sustainability and firm performance by analysing the literature as well as identifying clusters and links with the Sustainable Development Goals (SDGs). The top countries contributing to the research were China, India, and the United States, accounting for over 45% of the global publications. The study analysed a focused corpus of 65 documents from the Scopus database on specific subfields of corporate sustainability and firm performance, identifying five main thematic clusters related to environmental performance, financial performance, corporate sustainability reporting, corporate social performance, and green supply chain management, with significant citations related to 17 SDGs. The annual growth rate (AGR) of publications was found to be −2.88%, with an average of 4.06 publications per year. The relative growth rate (RGR) decreased from 0.69 in 2010 to 0.36 in 2023, and the doubling time (Dt.) increased from 1.00 in 2010 to 1.93 in 2023. Employing structured methods and the PRISMA protocol, this scientifically rigorous study points towards identification of research themes linking sustainability practices to firm performance. Exponential smoothing (Holt’s linear trend model) is employed to project future research output within the field. The significant trends include an increase in publication frequency since 2017, indicating a growth phase in the research field. The findings highlight the need for greater investigation from developing countries and the importance of integrating sustainability considerations into business strategies. Full article

Global economic growth, marked by rising GDP and population, has spurred demand for essential goods including furniture. This study presents a comprehensive demand forecasting analysis for retail furniture sales in the U.S. for the next 36 months using Multiple Linear Regression (MLR) and Holt–Winters methods. Leveraging retail sales data from 2019 to 2023, alongside key influencing factors such as furniture imports, consumer sentiment, and housing starts, we developed two predictive models. The results indicated that retail furniture sales exhibited strong seasonality and a positive trend, with the lowest forecasted demand in April 2024 (USD 9118 million) and the highest in December 2026 (USD 13,577 million). The average annual demand for 2024, 2025, and 2026 is projected at USD 12,122.5 million, USD 12,522.67 million, and USD 12,922.17 million, respectively, based on MLR, while Holt–Winters results are slightly more conservative. The models were compared using the Mean Absolute Percentage Error (MAPE) metric, with the MLR model yielding a MAPE of 3.47% and the Holt–Winters model achieving a MAPE of 4.21%. The study’s findings align with global market projections and highlight the growing demand trajectory in the U.S. furniture industry, providing valuable insights for strategic decision-making and operations management. Full article

The Wanjiang City Belt is an important part of Anhui’s economic development. It is the core area of the two national strategies regarding the rise of the central region and the integration of the Yangtze River Delta. This paper analyzes the urban development level of the Wanjiang City Belt using a nonparametric test. Holt’s linear trend method of a time series prediction model is used to predict and analyze the GDP growth rate of the second and third industries in the Wanjiang area. The results show that: (1) the economic development level of cities in the Wanjiang City Belt is unbalanced, and there is a significant gap in some cities in Jiangsu, Zhejiang and Shanghai; (2) the speed of undertaking industrial transfer in the Wanjiang City Belt is slowing down, and the competition of undertaking industrial transfer in the Wanjiang region is increasingly fierce; (3) in the process of the Wanjiang City Belt undertaking an industrial transfer, there are some problems such as the imbalance of undertaking ability, industrial isomorphism and regional competition, which hinder the coordinated development and sustainable economic development of the Wanjiang area. To achieve high-quality and sustainable development of the Wanjiang City Belt, it is necessary to further improve the policy guarantee, industrial cluster, talent introduction and independent innovation. Full article

Forecasting the industry’s electricity consumption is essential for energy planning in a given country or region. Thus, this study aims to apply time-series forecasting models (statistical approach and artificial neural network approach) to the industrial electricity consumption in the Brazilian system. For the statistical approach, the Holt–Winters, SARIMA, Dynamic Linear Model, and TBATS (Trigonometric Box–Cox transform, ARMA errors, Trend, and Seasonal components) models were considered. For the approach of artificial neural networks, the NNAR (neural network autoregression) and MLP (multilayer perceptron) models were considered. The results indicate that the MLP model was the one that obtained the best forecasting performance for the electricity consumption of the Brazilian industry under analysis. Full article

This research first aims to forecast tourist arrivals to Langkawi, Malaysia from its top three source markets, namely, China, Saudi Arabia, and the United Kingdom, between 2020 and 2022. Using the annual gross domestic product (GDP) growth of those three countries, the study seeks to investigate the impact of GDP on tourist arrivals from these countries to Langkawi in the context of post-COVID-19 scenarios. The study uses expert modelers, namely, ARIMA models and Holt’s linear models, to find the best fit model. Then, linear regression analysis was conducted to assess the impact of GDP on tourist arrivals in Langkawi from the said three countries. The results from the Holt linear model predicted a significant increase in the number of tourist arrivals from China and Saudi Arabia from 2020–2022. In contrast, the number of forecasted tourist arrivals from the United Kingdom would be on a decreasing trend from 2020–2022. It is also predicted that GDP growth will influence the tourist arrival trends from China and Saudi Arabia, but not for UK tourists. In other words, a speedy rate of recovery in the number of tourists from the UK to Langkawi is forecasted for once international travel restrictions are lifted, as the world eases into the post-pandemic period. Full article

Background: The COVID-19 pandemic has globally overwhelmed all sectors of life. The fast development of vaccines against COVID-19 has had a significant impact on the course of the pandemic. Methods: Global data from Google Trends was analyzed for vaccines against flu, BCG, HPV, pneumococcal disease, polio, and COVID-19. The time frame includes the last five-year period starting from 17 April 2016. Multiple training of time series models with back testing, including Holt–Winters forecasting, Exponential Smoothing State Space, Linear model with trend and seasonal components (tlsm), and ARIMA was conducted. Forecasting according to the best fitting model was performed. Results: Correlation analysis did not reveal a decrease in interest in vaccines during the analyzed period. The prediction models provided a short-term forecast of the dynamics of interest for flu, HPV, pneumococcal and polio vaccines with 5–10% growth in interest for the first quarter of 2022 when compared to the same quarter of 2021. Conclusions: Despite the huge interest in the COVID-19 vaccine, there has not been a detectable decline in the overall interest in the five analyzed vaccines. Full article

COVID-19 is one of the biggest challenges that countries face at the present time, as infections and deaths change daily and because this pandemic has a dynamic spread. Our paper considers two tasks. The first one is to develop a system for modeling COVID-19 based on time-series models due to their accuracy in forecasting COVID-19 cases. We developed an “Epidemic. TA” system using R programming for modeling and forecasting COVID-19 cases. This system contains linear (ARIMA and Holt’s model) and non-linear (BATS, TBATS, and SIR) time-series models and neural network auto-regressive models (NNAR), which allows us to obtain the most accurate forecasts of infections, deaths, and vaccination cases. The second task is the implementation of our system to forecast the risk of the third wave of infections in the Russian Federation. Full article

This paper focuses on the difference in inter-group and intra-group price of Yesso scallop (Patinopecten yessoensis) and the simulation accuracy of three different exponential smoothing models in the price. Based on the farm-gate price and wholesale price data of P. yessoensis in Changhai county from January 2017 to December 2018, this study uses the Wilcoxon rank sum test to compare the inter- and intra-group price and applies simple exponential smoothing (SES), Holt’s linear trend method, and Holt-Winters’ additive method to simulate and predict the price. The results suggest that (i) to improve economic benefits, it is necessary to formulate reasonable farming area and establish low-density ecological cultivation mode; (ii) the price’s Akaike information criterion (AIC) and mean absolute percentage error (MAPE) values by the SES model are optimal, and the MAPE value is lower than 4%; and (iii) the result of SES analysis shows no obvious change from January to March 2019. Full article

High-frequency monitoring of agrometeorological parameters is quintessential in the domain of Precision Agriculture (PA), where timeliness of collected observations and the ability to generate ahead-of-time predictions can substantially impact the crop yield. In this context, state-of-the-art internet-of-things (IoT)-based sensing platforms are often employed to generate, pre-process and assimilate real-time data from heterogeneous sensors and streaming data sources. Simultaneously, Time-Series Forecasting Algorithms (TSFAs) are responsible for generating reliable forecasts with a pre-defined forecast horizon and confidence. These TSFAs often rely on modelling the correlation between endogenous variables, the impact of exogenous variables on latent form and structural properties of data such as autocorrelation, periodicity, trend, pattern, and causality to approximate the model parameters. Traditionally, TSFAs such as the Holt–Winters (HW) and Autoregressive family of models (ARIMA) apply a linear and parametric approach towards model approximation, whilst models like Support Vector Regression (SVRs) and Neural Networks (NNs) adhere to a non-linear, non-parametric approach for modelling the historical data. Recently, Deep-Learning-based TSFAs such as Recurrent Neural Networks (RNNs), and Long-Short-Term-Memory (LSTMS) have gained popularity due to their capability to model long sequences of highly non-linear and stochastic data effectively. However, the evolution of TSFAs for predicting agrometeorological parameters pivots around one-step-ahead forecasting, which often overestimates the performance metrics defined for validating forecast capabilities of potential TSFAs. Hence, this paper attempts to evaluate and compare the performance of different machine learning (ML) and deep learning (DL) based TSFAs under one-step and multi-step-ahead forecast scenarios, thereby estimating the generalization capabilities of TSFA models over unseen data. The data used in this study are collected from an Automatic Weather Station (AWS), sampled at an interval of 15 min, and range over one month. Temperature (T) and Humidity (H) observations from the AWS are further converted into univariate, supervised time-series diurnal data profiles. Finally, walk-forward validation is used to evaluate recursive one-step-ahead forecasts until the desired prediction horizon is achieved. The results show that the Seasonal Auto-Regressive Integrated Moving Average (SARIMA) and SVR models outperform their DL-based counterparts in one-step and multi-step ahead settings with a fixed forecast horizon. This work aims to present a baseline comparison between different TSFAs to assist the process of model selection and facilitate rapid ahead-of-time forecasting for end-user applications. Full article

Continual energy availability is one of the prime inputs requisite for the persistent growth of any country. This becomes even more important for a country like India, which is one of the rapidly developing economies. Therefore electrical energy’s short-term demand forecasting is an essential step in the process of energy planning. The intent of this article is to predict the Total Electricity Consumption (TEC) in industry, agriculture, domestic, commercial, traction railways and other sectors of India for 2030. The methodology includes the familiar black-box approaches for forecasting namely multiple linear regression (MLR), simple regression model (SRM) along with correlation, exponential smoothing, Holt’s, Brown’s and expert model with the input variables population, GDP and GDP per capita using the software used are IBM SPSS Statistics 20 and Microsoft Excel 1997–2003 Worksheet. The input factors namely GDP, population and GDP per capita were taken into consideration. Analyses were also carried out to find the important variables influencing the energy consumption pattern. Several models such as Brown’s model, Holt’s model, Expert model and damped trend model were analysed. The TEC for the years 2019, 2024 and 2030 were forecasted to be 1,162,453 MW, 1,442,410 MW and 1,778,358 MW respectively. When compared with Population, GDP per capita, it is concluded that GDP foresees TEC better. The forecasting of total electricity consumption for the year 2030–2031 for India is found to be 1834349 MW. Therefore energy planning of a country relies heavily upon precise proper demand forecasting. Precise forecasting is one of the major challenges to manage in the energy sector of any nation. Moreover forecasts are important for the effective formulation of energy laws and policies in order to conserve the natural resources, protect the ecosystem, promote the nation’s economy and protect the health and safety of the society. Full article