Energy Time Series Forecasting Based on Pattern Sequence Similarity

This paper presents a new approach to forecast the behavior of time series based on similarity of pattern sequences. First, clustering techniques are used with the aim of grouping and labeling the samples from a data set. Thus, the prediction of a data point is provided as follows: first, the patter...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on knowledge and data engineering 2011-08, Vol.23 (8), p.1230-1243
Main Authors: Martinez Alvarez, Francisco, Troncoso, A, Riquelme, J C, Aguilar Ruiz, Jesus S
Format: Article
Language:English
Subjects:
Clustering
Data points
Electrostatic discharge
Exact sciences and technology
Forecasting
Inference from stochastic processes
time series analysis
Labels
Marking
Mathematical analysis
Mathematics
patterns
Probability and statistics
Sciences and techniques of general use
Silicon
Similarity
Statistics
Studies
Time series
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This paper presents a new approach to forecast the behavior of time series based on similarity of pattern sequences. First, clustering techniques are used with the aim of grouping and labeling the samples from a data set. Thus, the prediction of a data point is provided as follows: first, the pattern sequence prior to the day to be predicted is extracted. Then, this sequence is searched in the historical data and the prediction is calculated by averaging all the samples immediately after the matched sequence. The main novelty is that only the labels associated with each pattern are considered to forecast the future behavior of the time series, avoiding the use of real values of the time series until the last step of the prediction process. Results from several energy time series are reported and the performance of the proposed method is compared to that of recently published techniques showing a remarkable improvement in the prediction.
ISSN:1041-4347
1558-2191
DOI:10.1109/TKDE.2010.227