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There exist several data-driven approaches that enable us model time seriesdata including traditional regression-based modeling approaches (i.e., ARIMA).Recently, deep learning techniques have been introduced and explored in thecontext of time series analysis and prediction. A major research question toask is the performance of these many variations of deep learning techniques inpredicting time series data. This paper compares two prominent deep learningmodeling techniques. The Recurrent Neural Network (RNN)-based Long Short-TermMemory (LSTM) and the convolutional Neural Network (CNN)-based TemporalConvolutional Networks (TCN) are compared and their performance and trainingtime are reported. According to our experimental results, both modelingtechniques perform comparably having TCN-based models outperform LSTM slightly.Moreover, the CNN-based TCN model builds a stable model faster than theRNN-based LSTM models.

A Comparative Study of Detecting Anomalies in Time Series Data Using LSTM and TCN Models