Forecasting across time series databases using recurrent neural networks on groups of similar series: A clustering approach

作者：

Highlights：

• Building a model on a set of related time series can improve the forecast accuracy.

• Performance of the global models can degenerate if built on disparate time series.

• A subgrouping strategy then augments the accuracies of the baseline global models.

摘要

•Building a model on a set of related time series can improve the forecast accuracy.•Performance of the global models can degenerate if built on disparate time series.•A subgrouping strategy then augments the accuracies of the baseline global models.

论文关键词：Big data forecasting,RNN,LSTM,Time series clustering,Neural networks

论文评审过程：Received 28 April 2019, Revised 18 July 2019, Accepted 21 August 2019, Available online 26 August 2019, Version of Record 30 August 2019.

论文官网地址：https://doi.org/10.1016/j.eswa.2019.112896