Large computational quantity and cumulative error are main shortcomings of addweighted one-rank local-region single-step method for multi-steps prediction of chaotic time series. A local-region multi-steps forecasting model based on phase-space reconstruction is presented for chaotic time series prediction, including add-weighted one-rank local-region multisteps forecasting model and RBF neural...

More accurate forecasting of monthly rainfall is significantly important in drought forecasting in agriculture, irrigation schedule, water resources management, and crop pattern design. In this paper, ability of time series models in forecasting the rainfall according to the climate conditions is estimated. For this purpose, rainfall data of four different climates in Iran was selected. Using t...

The purpose – Accurately forecasting the demand for international health tourism is important to newly-emerging markets in the world. The aim of this study was presents a more suitable and accurate model for forecasting the demand for health tourism that should be more theoretically useful. Design – Applying GM(1,1) with adaptive levels of α (hereafter GM(1,1)-α model) to provide a concise pred...

This paper reports the study of the effect of the length of the recorded data used for monthly rainfall forecasting. Monthly rainfall data for three periods of 5, 10, and 49 years were collected from Kermanshah, Mashhad, Ahvaz, and Babolsar stations and used for calibration time series models. Then, the accuracy of the forecasting models was investigated by the following year’s data. The follow...

This paper evaluates the performance of prediction intervals generated from alternative time series models, in the context of tourism forecasting. The forecasting methods considered include the autoregressive (AR) model, the AR model using the bias-corrected bootstrap, seasonal ARIMA models, innovations state-space models for exponential smoothing, and Harvey’s structural time series models. We...

Most of existing fuzzy forecasting models partition historical training time series into fuzzy time series and build fuzzy-trend logical relationship groups to generate forecasting rules. The determination process of intervals is complex and uncertainty. In this paper, we present a novel fuzzy forecasting model based on high-order fuzzy-fluctuation trends and the fuzzy-fluctuation logical relat...

This paper investigates a range of statistical, neural network and hybrid approaches for making one-step-ahead forecasts of a monthly water demand time-series on the basis of 108 historical data points. A uni-variate approach, using solely the water demand time-series, is taken to construct two stand-alone forecasting models: a backpropagation network and a statistical model. A bi-variate appro...

In a competitive market environment, supply chain management (SCM) has been critical for companies to survive. Demand planning plays an important role in SCM, for it provides accurate demand forecasts which may achieve customer satisfaction by offering benefits such as low inventory level, short lead time, efficient resource allocation, and quick response. To obtain more accurate forecasts, thi...

short-term traffic flow forecasting plays a significant role in the intelligent transportation systems (its), especially for the traffic signal control and the transportation planning research. two mainly problems restrict the forecasting of urban freeway traffic parameters. one is the freeway traffic changes non-regularly under the heterogeneous traffic conditions, and the other is the success...

Forecasters in firms are expected to employ mathematical techniques encoded in information systems in order to predict the future demand for a firm=s goods. In practice, many forecasters have eschewed statistical methods of forecasting and depend instead on human expertise. This resistance to the ideals and technologies of forecasting has largely been understood in the literature as a failure o...