Summary of Evaluating the Effectiveness Of Predicting Covariates in Lstm Networks For Time Series Forecasting, by Gareth Davies
Evaluating the effectiveness of predicting covariates in LSTM Networks for Time Series Forecasting
by Gareth Davies
First submitted to arxiv on: 29 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed solution addresses the limitation of Autoregressive Recurrent Neural Networks (AR-RNNs) in integrating future, time-dependent covariates in multivariate time-series forecasting. Researchers conducted comprehensive tests on publicly available datasets, introducing highly correlated covariates at future time steps. The study compares the performance of an LSTM network considering these covariates to a univariate baseline. A novel approach using seasonal time segments and RNN architecture is introduced, demonstrating simple yet effective long-term forecast horizons comparable to state-of-the-art architectures. Results from over 120 models reveal that jointly training covariates with target variables can improve overall performance under certain conditions, but often exhibits significant performance disparity between multivariate and univariate predictions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at how well computers can predict future values in a series of numbers, like temperature or stock prices. It wants to know if giving the computer extra information about what’s going to happen later helps it make better predictions. The researchers tried different ways of doing this and found that sometimes it actually makes things worse! They used special kinds of computer programs called LSTM networks to test how well they could predict future values. |
Keywords
» Artificial intelligence » Autoregressive » Lstm » Rnn » Temperature » Time series
