DoubleAdapt: A Meta-learning Approach to Incremental Learning for Stock Trend Forecasting

arXiv:2306.09862v3 Announce Type: replace-cross
Abstract: Stock trend forecasting is a fundamental task of quantitative investment where precise predictions of price trends are indispensable. As an online service, stock data continuously arrive over time. It is practical and efficient to incrementally update the forecast model with the latest data which may reveal some new patterns recurring in the future stock market. However, incremental learning for stock trend forecasting still remains under-explored due to the challenge of distribution shifts (a.k.a. concept drifts). …

abstract arxiv cs.ai cs.ce cs.lg data forecast forecasting incremental investment meta meta-learning practical predictions price q-fin.cp q-fin.st quantitative service stock trend trends type update