Abstract
This is an overview paper, in which we briefly present results obtained over several years in the analysis, forecasting, and mining information from time series using methods that predominantly have nonstatistical character. Our main goal is to show the readers from the area of probability theory and statistics that nonstatistical methods can be pretty successful in time series processing. Besides the standard tasks such as estimation of trend/trend-cycle and forecasting, our methods are also powerful in providing additional information that can hardly be obtained using the statistical methods, namely, evaluation of the local course, finding perceptually important points, identification of structural breaks, finding periods of monotonous behavior including its evaluation, or summarization of information about large sets of time series. Our goal is not to beat statistical methods, but vice versa—to benefit from the synergy of both.
Published Version
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