A time series forecasting is an active research applied significantly in a variety of economics areas. Over the past three decades an auto-regressive integrated moving average (ARIMA) model, as one of the most important time series models, has been applied in financial markets forecasting. Recent researches in time series forecasting ARIMA models indicate some basic limitations which detract fr...

Abstract Forecasting electrical energy demand and consumption is one of the important decision-making tools in distributing companies for making contracts scheduling and purchasing electrical energy. This paper studies load consumption modeling in Hamedan city province distribution network by applying ESN neural network. Weather forecasting data such as minimum day temperature, average day temp...

Efficient and accurate sales forecasting is a vital part of creating an efficient supply chain in enterprises. Times series methods are a popular choice for forecasting demand sales. A major challenge is to develop a relatively inexpensive and automated forecasting engine that guarantees a desired forecasting accuracy. Times series decomposition and Forecast combination have been two classes of...

The multi-point values of an appropriate smoothing parameter of HP-filter algorithm for midterm electricity load demand (MELD) forecasting are proposed. The case study employs the data based on the organization of the Electricity Generating Authority of Thailand (EGAT). The research shows the growth at rate of weather and economic factors influencing to the electricity demand. The main focus of...

conventionally, regression and time series analyses have been employed in modeling water demand forecasts. in recent years, the relatively new technique of neural networks (nns) has been proposed as an efficient tool for modeling and forecasting. the objective of this study is to investigate the relatively new technique of gmdh – type neural networks for the use of forecasting long – term urban...

Demand forecasting; which sound basis for decision making process, is among the key activities that directly affect the supply chain performance. As the demand pattern varies from system to system, determination of the appropriate forecasting model that best fits the demand pattern is a hard decision in management of supply chains. The whiplash effect can be express as the variability of the de...

The paper presents a method for multivariate time series forecasting using Independent Component Analysis (ICA), as a preprocessing tool. The idea of this approach is to do the forecasting in the space of independent components (sources), and then to transform back the results to the original time series space. The forecasting can be done separately and with a different method for each componen...

This paper considers the problem of modelling and forecasting for multivariate financial time series. The use of Dynamic Linear State Space models and Stochastic Volatility models with Kalman filtering techniques to address this problem is considered in the context of providing a modular software implementation. The combination of these two approaches is presented with an illustrative example. ...

Automatic forecasts of large numbers of univariate time series are often needed in business and other contexts. We describe two automatic forecasting algorithms that have been implemented in the forecast package for R. The first is based on innovations state space models that underly exponential smoothing methods. The second is a step-wise algorithm for forecasting with ARIMA models. The algori...

agricultural prices have a high fluctuation and forecasting may help decision making effectively. the aim of this study was to forecast the nominal and real prices of sugar beet and to recognize the appropriate forecasting model. initially the stationary of the series was tested. in order to investigate whether the series are stochastic, the nonparametric test of vald-wulfowitz and parametric t...