Integration of energy systems with information technologies has facilitated the realization of smart energy systems that utilize information to optimize system operation. To that end, crucial in optimizing energy system operation is the accurate, ahead-of-time forecasting of load demand. In particular, load forecasting allows planning of system expansion, and decision making for enhancing syste...

Fuzzy time series model has been developed to either improve forecasting accuracy or reduce computation time, whereas a residul analysis in order to improve its forecasting performance is still lack of consideration. In this paper, we propose a novel Fourier method to revise the analysis of residual terms, and then we illustrate it to forecast the Japanese tourists visiting in Taiwan per year. ...

Conventionally, linear and univariate time series models are broadly used in earning forecast. However, their forecasting accuracies are seriously limited without considering sufficient important factors. On the other hand, using more variables does not guarantee to obtain better forecasting accuracy and may cause inefficiency. The Multi-Objective Genetic Algorithms (MOGA) have been shown to be...

The paper examines the problem of forecasting ongoing factory orders and monitoring retail demand, with speci®c reference to high-technology consumer durables. We present evidence of the managerial importance of the problem and, using a case study of a computer peripheral manufacturer, we describe how di€erent data sources and models can be used to increase prediction accuracy. First we examine...

A recent study compared the performance of neural network modeling to those of three traditional time series methods (simple exponential smoothing, Croston’s method, and a modification of Croston’s method) as applied to actual lumpy demand time series. The current study, which is an extension of the previous study, seeks to identify factors that may indicate relative performance of the alternat...

The least square support vector machines (LSSSVM) model is a novel forecasting approach and has been successfully used to solve time series problems. However, the applications of LSSVM model in a seasonal time series forecasting has not been widely investigated. This study aims at developing a LSSVM model to forecast seasonal time series data. To assess the effectiveness of this model, the airl...

Artificial Neural Networks are universal and highly flexible function approximators first used in the fields of cognitive science and engineering. In recent years, Neural Networks have become increasingly popular in finance for tasks such as pattern recognition, classification and time series forecasting. The ability to predict cash requirement within reasonable accuracy of actual demand provid...

This study compared the methods used to forecast increases in power consumption caused by rising popularity of electric vehicles (EVs). An excellent model for each region was proposed using multiple scaled geographical datasets over two years. EV charging volumes are influenced various factors, including condition a vehicle, battery’s state-of-charge (SOC), and distance destination. However, su...

The present study suggests a model for predicting liquidity gap, based on source and cost of funds approach concerning the daily time series data (25 March 2009 to 19 March 2018), in order to control and manage the liquidity risk. Using the family of autoregressive conditional heteroscedasticity models, the behavior of bank liquidity gap is modeled and predicted. The results show that the APGAR...

Current forecasting software can be problematic in its level of integration with Microsoft Excel forecasting models. Standalone forecasting software operates in a batch mode with Excel and is impossible to run interactively without custom programming. Excel forecasting add-ins are designed to work within Excel and can be automated to use with forecasting models, but lack support for damped tren...