Time Warping Techniques in Clustering Time Series
نویسندگان
چکیده
The problem of obtaining an accurate forecast is becoming more and more significant as the production possibilities and technologies evolve. The accuracy of a forecast mainly depends on the dataset used for forecasting as well as on the methods employed. This study is devoted to the time series analysis. A set of known methods and techniques are used for analysing the time series; one of them is the Kohonen self-organising maps. The Time Warping techniques enable SOM to cluster time series of different duration, which is highly significant in product life cycle analysis and phase switching tasks. The main goal of the research is to perform a set of experiments aimed at comparing the efficiency of several Time Warping techniques and seeing how the chosen topology of neurons in the Self-organising map influences the final forecasting result.
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