Similarity search in time series data is used in diverse domains. The most prominent work has focused on similarity search considering either complete time series or certain subsequences of time series. Often, time series like temperature measurements consist of periodic patterns, i.e. patterns that repeatedly occur in defined periods over time. For example, the behavior of the temperature with...

Pattern modelling in time-series prediction refers to the process of identifying past relationships and trends in historical data for predicting future values. This paper describes the development of a new pattern matching technique for univariate time-series forecasting. The pattern modelling technique out-performs frequently used statistical methods such as Exponential Smoothing on different ...

In this paper, we propose some new tools to allow machine learning classiiers to cope with time series data. We rst argue that many time-series classiication problems can be solved by detecting and combining local properties or patterns in time series. Then, a technique is proposed to nd patterns which are useful for classiication. These patterns are combined to build interpretable classiicatio...

Abstract In order to effectively mine the structural features in time series and simplify complexity of analysis, equiprobable symbolization pattern entropy (EPSPE) is proposed this paper. The original are implemented through symbolic processing according an equal probability distribution. Then, sliding window technique used obtain a finite number different patterns, pairs determined by calcula...