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About Pplied Time Series Ransfer Learning
This paper proposes applying transfer learning to time series modeling using an LSTM autoencoder with attention. The authors show that a model trained on a large preprocessed time series dataset can extract generalized features that improve classification, disaggregation, and forecasting on other datasets. Specifically: - An LSTM autoencoder with attention is trained on a large preprocessed time series dataset to learn generalized features. - These features improve classification accuracy on a benchmark dataset compared to traditional time series features. - The model, fine-tuned on individual time series, improves performance on disaggregation and forecasting of other datasets compared to not using transfer learning. - The authors aim to make the pretrained time series feature extraction model publicly available, as
Detailed Information
| Author: | ['M.Qasim Mahmood'] |
|---|---|
| Publication Year: | 2020 |
| Pages: | 4 |
| Language: | English |
| Format: | |
| Price: | FREE |
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