2021/12/17 by Saroj Gopali, Faranak Abri, Gopali, Saroj +5 · 1 citation
Computer Science · #Anomaly Detection Techniques and Applications #Data Stream Mining Techniques #FOS: Computer and information sciences #Machine Learning (cs.LG) #Performance (cs.PF) #Time Series Analysis and Forecasting
paper · pdf · doi:10.48550/arxiv.2112.09293
openalex publication_date 2021/12/17 · openalex created_date 2022/05/05 · openalex updated_date 2026/07/28
There exist several data-driven approaches that enable us model time series data including traditional regression-based modeling approaches (i.e., ARIMA). Recently, deep learning techniques have been introduced and explored in the context of time series analysis and prediction. A major research question to ask is the performance of these many variations of deep learning techniques in predicting time series data. This paper compares two prominent deep learning modeling techniques. The Recurrent Neural Network (RNN)-based Long Short-Term Memory (LSTM) and the convolutional Neural Network (CNN)-based Temporal Convolutional Networks (TCN) are compared and their performance and training time are reported. According to our experimental results, both modeling techniques perform comparably having TCN-based models outperform LSTM slightly. Moreover, the CNN-based TCN model builds a stable model faster than the RNN-based LSTM models.