Performance evaluation of deep neural networks for forecasting time‐series with multiple structural breaks and high volatility
Abstract The problem of automatic and accurate forecasting of time‐series data has always been an interesting challenge for the machine learning and forecasting community. A majority of the real‐world time‐series problems have non‐stationary characteristics that make the understanding of trend and s...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-09-01
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| Series: | CAAI Transactions on Intelligence Technology |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/cit2.12002 |