It is well known that coalmine gas concentration forecasting is very significant to ensure the safety of mining. Owing to the high-frequency, non-stationary, fluctuations, and chaotic properties of the gas concentration time series, a gas concentration forecasting model utilizing the original raw data often leads to an inability to provide satisfying forecast results. A hybrid forecasting model...

Forecasting of silicon content in blast furnace (BF) hot metal has always been an important tool in the control of iron-making process. To get an accurate prediction of silicon content is an urgent task for BF operators. The approach based on generalized autoregressive conditional heteroskedastic (GARCH) has been introduced to predict step-ahead silicon content in BF hot metal. The algorithm ha...

Training neural networks to capture an intrinsic property of a large volume of high dimensional data is a difficult task, as the training process is computationally expensive. Input attributes should be carefully selected to keep the dimensionality of input vectors relatively small. Technical indexes commonly used for stock market prediction using neural networks are investigated to determine i...

This paper proposes a Multiples Input-Multiples Ouput Autoregressive (MIMO-AR) model based on two stages to improve monthly anchovy catches forecasting of the coastal zone of Chile for periods from January 1958 to December 2011. In the first stage, the stationary wavelet transform (SWT) based on Fejer-Korovkin (FK) wavelet filter is used to separate the raw time series into a high frequency (HF...

The traditional multi-step ahead prediction is based on sequential algorithm to run multi-step ahead prediction and it brings error propagation problem. Furthermore, the prediction error of multi-step ahead includes both system and propagation errors. Therefore, how to decrease the propagation error has become an important issue in multi-step ahead prediction. In this study we had used the para...

In this paper the problem of one-step ahead prediction of the critical frequency (f oF2) of the middlelatitude ionosphere, using time series forecasting methods, is considered. The whole study is based on a sample of about 58 000 observations of f oF2 with 15-min time resolution, derived from the Athens digisonde ionograms taken from the Digisonde Portable Sounder (DPS4) located at Palaia Pente...

The most common forecasting methods in business are based on exponential smoothing, and the most common time series in business are inherently non-negative. Therefore it is of interest to consider the properties of the potential stochastic models underlying exponential smoothing when applied to non-negative data. We explore exponential smoothing state space models for non-negative data under va...

This paper presents an artificial neural network(ANN) approach to electric load forecasting. The ANN is used to learn the relationship among past, current and future temperatures and loads. In order to provide the forecasted load, the ANN interpolates among the load and temperature data in a training data set. The average absolute errors of the one-hour and 24-hour ahead forecasts in our test o...

ahead forecasting of hydrological processes Georgia A. Papacharalampous*, Hristos Tyralis and Demetris Koutsoyiannis Department of Water Resources and Environmental Engineering, School of Civil Engineering, National Technical University of Athens, Iroon Polytechniou 5, 157 80 Zografou, Greece * Corresponding author, [email protected] Abstract: We perform an extensive comparison...

We compare the predication performance of neural networks with the different frequencies of input data, namely daily data, weekly data, monthly data. In the 1 day and 1 week ahead prediction of foreign exchange rates forecasting, the neural networks with the weekly input data performs better than the random walk models. In the 1 month ahead prediction of foreign exchange rates forecasting, only...