An empirical study of neural networks for trend detection in time series

Alexandre Miot and Gilles Drigout

Papers from arXiv.org

Abstract: Detecting structure in noisy time series is a difficult task. One intuitive feature is the notion of trend. From theoretical hints and using simulated time series, we empirically investigate the efficiency of standard recurrent neural networks (RNNs) to detect trends. We show the overall superiority and versatility of certain standard RNNs structures over various other estimators. These RNNs could be used as basic blocks to build more complex time series trend estimators.

Date: 2019-12, Revised 2020-02
New Economics Papers: this item is included in nep-big, nep-cmp and nep-ets
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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:1912.04009

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