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...

In this paper we propose a computationally effective approach to detect multiple structural breaks in the mean occurring at unknown dates. We propose a non-parametric approach that exploits, in the framework of least squares regression trees, the contiguity property of the Fisher grouping method (1958) proposed for grouping a single real variable. The proposed approach is applied to study the p...

Streamflow forecasting is very important for water resources management and flood defence. In this paper two forecasting methods are compared: ARIMA versus a multilayer perceptron neural network. This comparison is done by forecasting a streamflow of a Mexican river. Surprising results showed that in a monthly basis, ARIMA has lower prediction errors than this Neural Network. Key-Words: Auto re...

This special issue of Model Assisted Statistics and Applications (MASA) focused on knowing how current machine learning methods can be applied to diverse statistics areas. We have ten papers about the recent machine learning developments and applications, including survey sampling, biostatistics, bioinformatics, genetics, time series analysis, and technology forecasting. The issue starts with a...

In the framework of TEI@I methodology, this paper proposes a combined forecast method integrating contextual knowledge (CFMIK). With the help of contextual knowledge, this method considers the effects of those factors that cannot be explicitly included in the forecast model, and thus it can efficiently decrease the forecast error resulted from the irregular events. Through a container throughpu...

As a versatile investment tool in energy markets for speculators and hedgers, the Goldman Sachs Commodity Index (GSCI) futures are quite well known. Therefore, this paper proposes a hybrid model incorporating ARCH family models and ANN model to forecast GSCI futures price. Empirical results show that the hybrid ARCH(1)-M-ANN model is superior to ARIMA, ARCH(1),GARCH(1,1), EGARCH(1,1) and ARIMA-...

The predictability of network traffic is a significant interest in many domains such as congestion control, admission control, and network management. An accurate traffic prediction model should have the ability to capture prominent traffic characteristics, such as long-range dependence (LRD) and self-similarity in the large time scale, multifractal in small time scale. In this paper we propose...

With the increasing competition in the telecommunications industry, the operators try their best to increase telecom income via various measures, one of which is to set an amount of income as a goal to make the encouragement. Since accurate forecast of income plays an important role in income target setting, this paper builds a time series Autoregressive Integrated Moving Average Model (ARIMA) ...

For example, it has long been recognized that single exponential forecasting (SES) is equivalent to an ARIMA(0,1,1) model (e.g., Harvey, 1989) The additional flexibility of ARIMA models may be thought to lead to more accurate empirical forecasts. However, Table 13 of Makridakis and Hibon shows that there is virtually no improvement in forecasting accuracy using ARIMA models (labeled B-J automat...

Analyses from some of the highway agencies show that up to 50% permanent traffic counts (PTCs) have missing values. It will be difficult to eliminate such a significant portion of data from traffic analysis. Literature review indicates that the limited research uses factor or autoregressive integrated moving average (ARIMA) models for predicting missing values. Factor-based models tend to be le...