One of the main problems in chaotic time series prediction is that the underlying nonlinear dynamics is usually unknown. Using a nonlinear predictor to predict a chaotic time series usually puts a limit on the accuracy since the nonlinear predictor is basically an approximation of the unknown nonlinear mapping. In this paper, we propose using fusion of predictors as a method to improve the perf...

The problem of chaotic time series prediction is studied in various disciplines now including engineering, medical and econometric applications. Chaotic time series are the output of a deterministic system with positive Liapunov exponent. A time series prediction is a suitable application for a neuronal network predictor. The NN approach to time series prediction is non-parametric, in the sense...

Yong Wang, Shiqiang Hu* School of Aeronautics and Astronautics Shanghai Jiao Tong University, Shanghai [email protected], [email protected] Abstract—In this paper, a novel prediction algorithm is proposed to predict chaotic time series. The chaotic time series can be embedded into state space by Takens embedding theorem. The one dimensional data is mapped to a higher dimensional space that pr...

The time series forecast is a very complex problem, consisting in predicting the behaviour of a data series with only the information of the previous sequence. There is many physical and artificial phenomenon that can be described by time series. The prediction of such phenomenon could be very complex. For instance, in the case of tide forecast, unusually high tides, or sea surges, result from ...

Large computational quantity and cumulative error are main shortcomings of addweighted one-rank local-region single-step method for multi-steps prediction of chaotic time series. A local-region multi-steps forecasting model based on phase-space reconstruction is presented for chaotic time series prediction, including add-weighted one-rank local-region multisteps forecasting model and RBF neural...

This paper gives the quantitative relationship between prediction error and given past sample size in our research of sea level time series. The present result exhibits that the prediction error of sea level time series in terms of given past sample size follows decayed power functions, providing a quantitative guideline for the quality control of sea level prediction.

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...

For independent samples, shrinkage estimation theory has been developed systematically. Although shrinkage estimators are biased, they improve the MSE of unbiased ones. In view of this, we will develop shrinkage estimation theory and prediction for dependent samples. First, we propose a shrinkage estimator for the coefficients of AR model, which improves the MSE of the least squares estimator. ...

In this paper we present some work on trying to apply propositional learning systems to the problem of time series prediction. We describe the difficulties of trying to adapt classification systems to prediction problems. Some techniques to perform this adaptation are presented. These techniques are based on the introduction of new attributes that convey relevant information about the temporal ...