Dynamic Similar Sub-Series Selection Method for Time Series Forecasting

Dynamic time-series forecasting using local approximation

Pattern recognition techniques for time-series forecasting are beginning to be realised as an important tool for predicting chaotic behaviour of dynamic systems. In this paper we develop the concept of a Pattern Modelling and Recognition System which is used for predicting future behaviour of time-series using local approximation. In this paper we compare this forecasting tool with neural netwo...

Nonparametric time series forecasting with dynamic updating

We present a nonparametric method to forecast a seasonal univariate time series, and propose four dynamic updating methods to improve point forecast accuracy. Our methods consider a seasonal univariate time series as a functional time series. We propose first to reduce the dimensionality by applying functional principal component analysis to the historical observations, and then to use univaria...

Self-Similar Extrapolation of Asymptotic Series and Forecasting for Time Series

The method of extrapolating asymptotic series, based on the Self-Similar Approximation Theory, is developed. Several important questions are answered, which makes the foundation of the method unambiguous and its application straightforward. It is shown how the extrapolation of asymptotic series can be reformulated as forecasting for time series. The probability measure is introduced characteriz...

Ensembles for Time Series Forecasting

This paper describes a new type of ensembles that aims at improving the predictive performance of these approaches in time series forecasting. Ensembles are recognised as one of the most successful approaches to prediction tasks. Previous theoretical studies of ensembles have shown that one of the key reasons for this performance is diversity among ensemble members. Several methods exist to gen...

Model selection for weakly dependent time series forecasting