Applied timeseries Transfer learning

Nikolay Laptev, Jiafan Yu, Ram Rajagopal

Feb 12, 2018 (modified: Jun 04, 2018) ICLR 2018 Workshop Submission readers: everyone Show Bibtex
  • Abstract: Reliable and accurate time-series modeling is critical in many fields including energy, finance, and manufacturing. Many time-series tasks, however, suffer from a limited amount of clean training data resulting in poor forecasting, classification or clustering performance. Recently, convolutional neural networks (CNNs) have shown outstanding image classification performance even on tasks with small-scale training sets. The performance can be attributed to transfer learning through ability of CNNs to learn rich mid-level image representations. For time-series, however, no prior work exists on general transfer learning. In this short paper, motivated by recent success of transfer learning in image-related tasks, we are the first to show that using an LSTM auto-encoder with attention trained on a large-scale timeseries dataset with pre-processing we can effectively transfer time-series features across diverse domains.
  • Keywords: time-series, transfer-learning
  • TL;DR: transfer learning in time-series

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