Iterative time series prediction and analysis by embedding and multiple time-scale decomposition networks

Neep Hazarika; David Lowe

doi:10.1117/12.271470

4 April 1997 Iterative time series prediction and analysis by embedding and multiple time-scale decomposition networks

Neep Hazarika, David Lowe

Proceedings Volume 3077, Applications and Science of Artificial Neural Networks III; (1997) https://doi.org/10.1117/12.271470
Event: AeroSense '97, 1997, Orlando, FL, United States

Abstract

In this work we describe a method of estimating and characterizing appropriate data and model complexity in the context of long term iterated time series forecasting using embeddings and multiple time-scale decomposition techniques. An embedding of a signal is obtained which decouples multiple time scale effects such as seasonality and trend. The complexity and stability of networks are estimated and the performance of long term iteration is examined. The performance of the technique is tested using the real world time series problems of electricity load forecasting, and financial futures contracts.

Citation Download Citation

Neep Hazarika and David Lowe "Iterative time series prediction and analysis by embedding and multiple time-scale decomposition networks", Proc. SPIE 3077, Applications and Science of Artificial Neural Networks III, (4 April 1997); https://doi.org/10.1117/12.271470

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