Time series; Fuzzy transform; Evaluative linguistic expressions; Fuzzy natural logic; Mining information from time series

This is an overview paper, in which we briefly present results obtained 4 over several years in the analysis, forecasting, and mining information from time 5 series using methods that predominantly have nonstatistical character. Our main 6 goal is to show the readers from the area of probability theory and statistics that 7 nonstatistical methods can be pretty successful in time series processing. Besides 8 the standard tasks such as estimation of trend/trend-cycle and forecasting, our 9 methods are also powerful in providing additional information that can hardly be 10 obtained using the statistical methods, namely, evaluation of the local course, finding 11 perceptually important points, identification of structural breaks, finding periods 12 of monotonous behavior including its evaluation, or summarization of information 13 about large sets of time series. Our goal is not to beat statistical methods, but vice 14 versa—to benefit from the synergy of both.