Financial Time Series Data Processing for Machine Learning

Fabrice Daniel

Papers from arXiv.org

Abstract: This article studies the financial time series data processing for machine learning. It introduces the most frequent scaling methods, then compares the resulting stationarity and preservation of useful information for trend forecasting. It proposes an empirical test based on the capability to learn simple data relationship with simple models. It also speaks about the data split method specific to time series, avoiding unwanted overfitting and proposes various labelling for classification and regression.

Date: 2019-07
New Economics Papers: this item is included in nep-big, nep-cmp, nep-ecm and nep-pay
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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:1907.03010

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