Title
Multi-Step Time Series Forecasting With An Ensemble Of Varied Length Mixture Models
Abstract
Many real-world problems require modeling and forecasting of time series, such as weather temperature, electricity demand, stock prices and foreign exchange (FX) rates. Often, the tasks involve predicting over a long-term period, e.g. several weeks or months. Most existing time series models are inheritably for one-step prediction, that is, predicting one time point ahead. Multi-step or long-term prediction is difficult and challenging due to the lack of information and uncertainty or error accumulation. The main existing approaches, iterative and independent, either use one-step model recursively or treat the multi-step task as an independent model. They generally perform poorly in practical applications. In this paper, as an extension of the self-organizing mixture autoregressive (AR) model, the varied length mixture (VLM) models are proposed to model and forecast time series over multi-steps. The key idea is to preserve the dependencies between the time points within the prediction horizon. Training data are segmented to various lengths corresponding to various forecasting horizons, and the VLM models are trained in a self-organizing fashion on these segments to capture these dependencies in its component AR models of various predicting horizons. The VLM models form a probabilistic mixture of these varied length models. A combination of short and long VLM models and an ensemble of them are proposed to further enhance the prediction performance. The effectiveness of the proposed methods and their marked improvements over the existing methods are demonstrated through a number of experiments on synthetic data, real-world FX rates and weather temperatures.
Year
DOI
Venue
2018
10.1142/S0129065717500538
INTERNATIONAL JOURNAL OF NEURAL SYSTEMS
Keywords
Field
DocType
Time series forecasting, long-term prediction, probabilistic mixture model, regressive models, self-organizing networks
Autoregressive model,Time series,Time point,Pattern recognition,Long-term prediction,Computer science,Self-organizing network,Probabilistic forecasting,Artificial intelligence,Machine learning,Mixture model,Recursion
Journal
Volume
Issue
ISSN
28
4
0129-0657
Citations 
PageRank 
References 
1
0.34
24
Authors
2
Name
Order
Citations
PageRank
Yicun Ouyang110.34
Hujun Yin21577149.88