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Time Series Forecasting Using LSTM Networks: A Symbolic Approach

Authors :
Elsworth, Steven
Güttel, Stefan
Publication Year :
2020

Abstract

Machine learning methods trained on raw numerical time series data exhibit fundamental limitations such as a high sensitivity to the hyper parameters and even to the initialization of random weights. A combination of a recurrent neural network with a dimension-reducing symbolic representation is proposed and applied for the purpose of time series forecasting. It is shown that the symbolic representation can help to alleviate some of the aforementioned problems and, in addition, might allow for faster training without sacrificing the forecast performance.<br />Comment: 12 pages, 17 figures

Details

Database :
arXiv
Publication Type :
Report
Accession number :
edsarx.2003.05672
Document Type :
Working Paper