Prediction of Traffic Generated by IoT Devices Using Statistical Learning Time Series Algorithms

Time Series Prediction using ICA Algorithms

In this paper we propose a new method for volatile time series forecasting using Independent Component Analysis (ICA) algorithms and SavitzkyGolay filtering as preprocessing tools. The preprocessed data will be introduce in a based radial basis functions (RBF) Artificial Neural Network (ANN) and the prediction result will be compared with the one we get without these preprocessing tools or the ...

Machine learning algorithms for time series in financial markets

This research is related to the usefulness of different machine learning methods in forecasting time series on financial markets. The main issue in this field is that economic managers and scientific society are still longing for more accurate forecasting algorithms. Fulfilling this request leads to an increase in forecasting quality and, therefore, more profitability and efficiency. In this pa...

FINANCIAL TIME SERIES PREDICTION USING MACHINE LEARNING ALGORITHMS by LESLIE TIONG

Some New Methods for Prediction of Time Series by Wavelets

Extended Abstract. Forecasting is one of the most important purposes of time series analysis. For many years, classical methods were used for this aim. But these methods do not give good performance results for real time series due to non-linearity and non-stationarity of these data sets. On one hand, most of real world time series data display a time-varying second order structure. On th...

Time Series Prediction of Telephone Traffic Occupancy using Neural Networks

Various techniques, including feed-forward neural networks, are applied to the time series prediction problem. The forecasting of occupancy on a telephone trunk group is taken as a case study. The relative performances of the techniques are reported. Theoretical justifications are provided for the results.