Extraction of informative statistical features in the problem of forecasting time series generated by It{\^{o}}-type processes

TL;DR

This paper develops methods to extract informative features from Itô process-based time series using mixture models and statistical reconstruction, enhancing prediction accuracy with simple autoregressive models.

Contribution

It introduces novel algorithms for reconstructing process coefficients and extracting features that improve time series forecasting accuracy.

Findings

Additional features improve autoregressive prediction performance.

Non-uniform reconstruction captures dependence of coefficients on process values.

Uniform techniques produce process-independent parameters.

Abstract

In this paper, we consider the problem of extraction of most informative features from time series that are regarded as observed values of stochastic processes satisfying the It{\^{o}} stochastic differential equations with unknown random drift and diffusion coefficients. We do not attract any additional information and use only the information contained in the time series as it is. Therefore, as additional features, we use the parameters of statistically adjusted mixture-type models of the observed regularities of the behavior of the time series. Several algorithms of construction of these parameters are discussed. These algorithms are based on statistical reconstruction of the coefficients which, in turn, is based on statistical separation of normal mixtures. We obtain two types of parameters by the techniques of the uniform and non-uniform statistical reconstruction of the coefficients of the underlying It{\^{o}} process. The reconstructed coefficients obtained by uniform techniques do not depend on the current value of the process, while the non-uniform techniques reconstruct the coefficients with the account of their dependence on the value of the process. Actually, the non-uniform techniques used in this paper represent a stochastic analog of the Taylor expansion for the time series. The efficiency of the obtained additional features is compared by using them in the autoregressive algorithms of prediction of time series. In order to obtain pure conclusion that is not affected by unwanted factors, say, related to a special choice of the architecture of the neural network prediction methods, we used only simple autoregressive algorithms. We show that the use of additional statistical features improves the prediction.