Deep reservoir computing based on self-rectifying memristor synapse for time series prediction

Using reservoir computing in a decomposition approach for time series prediction

In this paper we combine wavelet decomposition and recurrent neural networks to provide fast and accurate time series predictions. The original time series is decomposed by means of wavelet decomposition into a hierarchy of time series which are easier to predict. The prediction core of our solution is given by reservoir computing, which is a recently developed technique for the very fast train...

A comparative study of Reservoir Computing strategies for monthly time series prediction

A good prediction of the future enables companies and governments to plan their investments, production and other needs. The demand for good forecasting techniques motivates many researchers coming from a wide variety of fields to develop methods for time series prediction. Many of these techniques are very complex to apply and demand lots of computational effort to execute. As an answer to thi...

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...

Activity Prediction Based on Time Series Forcasting

Activity recognition is a crucial step in automatic assistance for elderly and disabled people, such as Alzheimer’s patients. The large number of activities of daily living (ADLs) that these persons are used to performing as well as their inability, sometimes, to start an activity make the recognition process difficult, if not impossible. To adress such problems, we propose a timebased activity...

Self-similar Based Time Series Analysis and Prediction