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Abstract.The paper considers the problem of large processing huge amounts of data for temperature monitoring of man-made and natural objects associated with the lack of data compression efficiency in real time when they are transferred and stored in the presence of anomalies in the information signal in the form of sudden changes and outlier. The solutions of existing methods were described and new approaches were proposed. The results of experimental comparison of proposed and known solutions are included.

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. In this paper, a method for detecting the weather temperature series periodicity is proposed. 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.

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