Graph Based Framework for Time Series Prediction

Vivek Yadav, Durga Toshniwal

Abstract


Purpose: A time series comprises of a sequence of observations ordered with time. A major task of data mining with regard to time series data is predicting the future values. In time series there is a general notion that some aspect of past pattern will continue in future. Existing time series techniques fail to capture the knowledge present in databases to make good assumptions of future values.

Design/Methodology/Approach: Application of graph matching technique to time series data is applied in the paper.

Findings: The study found that use of graph matching techniques on time-series data can be a useful technique for finding hidden patterns in time series database.

Research Implications: The study motivates to map time series data and graphs and use existing graph mining techniques to discover patterns from time series data and use the derived patterns for making predictions.

Originality/Value: The study maps the time-series data as graphs and use graph mining techniques to discover knowledge from time series data.

Keywords: Data mining; Time Series Prediction; Graph Mining; Graph MatchingPaper

Type: Conceptual


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