Mathematics & Physics 2015, 8(2), 157–164 УДК 53.087:004.021 Detection of Regularity Violations of Cyclic Processes in a Temperature Monitoring System Using Patterns Form Hussein Sh. <...> Hussein Alexey G.Yakunin∗ Information Technologies Faculty Altai State Technical University Lenin av., 46, Barnaul, 656038 Russia Received 29.01.2015, received in revised form 04.02.2015, accepted 13.03.2015 Periodicity mining is used for predicting trends in time series data. <...> Discovering the rate at which the time series is periodic has always been an obstacle for fully automated periodicity mining. <...> The proposed method, based on DFT, effectively discovered the series periodicity and determined the periodic patterns and their repetition frequencies. <...> Then, the series has been divided into equal time slots based on the pattern repetition frequency. <...> A reference series has been constructed as repetitions for a template pattern, which was constructed from the patterns averages of the original temperature series. <...> The reference series is very useful in temperature series analysis, as the patterns deviations, the future patterns predictions, and the anomalies detections. <...> Experimental results show that the proposed method accurately discovers periodicity rates and periodic patterns. <...> Introduction Periodicity mining is a method that helps in predicting the behavior of time series data [1] Many data mining researches concerned to identifying and extracting different types of patterns in massive data. <...> Time series analysis has an important role in weather measurements changes analysis. <...> It has been carried out for various weather parameters such as air temperature [13, 14] and rainfall data analysis [15], The periodicity analysis of temperature time series is very important to study the effects of climate change. <...> Hussein, Alexey G.Yakunin Detection of Regularity Violations of Cyclic Processes . • Matlab will be used for all simulation and experimental analysis. 1. <...> Moving average, themost common smoothing algorithm, will be applied here. • Measured Data normalization To concentrate analysis on the series fluctuations, the measured temperature samples “f(t)” will be normalized with Z-scores [16], the most commonly used <...>