The Time Series Forecasting Method based on Causal Convolution and RNN
DOI:
https://doi.org/10.54097/cpl.v11i1.10191Keywords:
Enter Time series forecasting; Deep learning; Convolutional neural nets; Recurrent neural nets; Transformer nets.Abstract
The analysis of time series in order to discover their intrinsic regularities and to predict future developments is an important academic and applied tool. With the development of sensor and network technology, how to perform accurate and efficient predictive analysis based on large amounts of historical time-series data has become an urgent problem. Time series forecasting has developed rapidly in recent years, with deep learning techniques being used extensively to produce research results. This paper provides an overview of the common characteristics and metrics used to evaluate time series, The traditional methods and machine learning methods involved in time series forecasting are discussed and the applications of deep learning methods, including convolutional neural networks(CNN),recurrent neural networks(RNN) and transformer networks, in time forecasting are highlighted and the advantages and disadvantages of various deep learning based time series methods are compared.
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