The practical utilization of available modern as well as traditional weather forecasting systems builds herders' resiliency capacity to climatic shocks. The precision and reliability of the forecasting system determines its creditability and acceptance by the users to be proactive in the decisions they make based on the forecasted information. It has been postulated that traditional weather for...

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...

Improving time series forecastingaccuracy is an important yet often difficult task.Both theoretical and empirical findings haveindicated that integration of several models is an effectiveway to improve predictive performance, especiallywhen the models in combination are quite different. In this paper,a model of the hybrid artificial neural networks andfuzzy model is proposed for time series for...

The contamination of shellfish harvesting areas by fecal bacteria in the Annapolis Basin of Nova Scotia, Canada, is a recurring problem which has consequences for industry, government, and local communities. This study contributes to the development of an integrated water quality forecasting system to improve the efficiency and effectiveness of industry management. The proposed integrated forec...

While hazardous chemicals pollutions occurring, monitoring and forecasting are very important for emergency planning and evacuation. To implement the object, huge amounts of wireless sensors may be distributed in large area. The data gathered by the sensors are sent back to data centers for preprocessing and analysis, which need massive computing utility. Therefore a high effective computing pa...

In the recent years there is a lot of research happening to predict wind speed with several mathematical methods and biologically inspired computing techniques to reduce the prediction error. A new strategy in wind speed prediction is proposed in this paper and the Adaptive Neuro-Fuzzy Inference System (ANFIS) is applied to forecast the speed of wind. ANFIS can forecast very well of the next va...

This paper develops a novel approach to modeling and forecasting realized volatility (RV) measures based on copula functions. Copula-based time series models can capture relevant characteristics of volatility such as nonlinear dynamics and long-memory type behavior in a flexible yet parsimonious way. In an empirical application to daily volatility for S&P500 index futures, we find that the copu...

The aim of this contribution is to show the opportunities of applying of fuzzy time series models to predict multiple heterogeneous time series, given at International Time Series Forecasting Competition [http://irafm.osu.cz/cif/main.php]. The dataset of this competition includes 91 time series of different length, time frequencies and behaviour. In this paper the framework (algorithm) of multi...