In recent years, various time series models have been proposed for financial markets forecasting. In each case, the accuracy of time series forecasting models are fundamental to make decision and hence the research for improving the effectiveness of forecasting models have been curried on. Many researchers have compared different time series models together in order to determine more efficien...

The price of fresh agricultural products changes up and down recently. In order to accurately forecast the agricultural precuts demand, a forecasting model based on ARIMA is provided in this study. It can be found that asymmetric information and unbalance about supply and demand exist in the market through analyzing the reasons. The ARIMA model for fresh agricultural products can forecast the d...

We analyze the effects on prediction intervals of fitting ARIMA models to series with stochastic trends, when the underlying components are heteroscedastic. We show that ARIMA prediction intervals may be inadequate when only the transitory component is heteroscedastic. In this case, prediction intervals based on the unobserved component models tend to the homoscedastic intervals as the predicti...

The integration of renewable energy resources into smart grids has become increasingly important to address the challenges managing and forecasting production in fourth revolution. To this end, artificial intelligence (AI) emerged as a powerful tool for improving control management. This study investigates application machine learning techniques, specifically ARIMA (auto-regressive integrated m...

This research work has addressed demand side flexibility in a smart grid oriented building. The principal purpose has been to build a short term forecasting model that will predict the next hour consumption. Three advanced methods of forecasting have been investigated for this purpose, the ARIMA (Autoregressive Integrated Moving Average) model, Artificial Neural Networks (ANN) and Support Vecto...

Network traffic prediction (NTP) represents an essential component in planning large-scale networks which are general unpredictable and must adapt to unforeseen circumstances. In small medium-size networks, the administrator can anticipate fluctuations without need of using forecasting tools, but scenario where hundreds new users be added a matter weeks, more efficient tools required avoid cong...