May 26, 2022 · Specifically, Transformers is arguably the most successful solution to extract the semantic correlations among the elements in a long sequence.
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Specifically, Transformers is arguably the most successful so- lution to extract the semantic correlations among the elements in a long sequence. However, in ...
Jan 3, 2023 · It seems to cast doubt on the recent trend of using transformers for time series forecasting, suggesting a simple model can out perform complex transformers.
Yes, Transformers are Effective for Time Series Forecasting (+ ...
huggingface.co › blog › autoformer
Jun 16, 2023 · We will provide empirical evidence that Transformers are indeed Effective for Time Series Forecasting. Our comparison shows that the simple linear model, known ...
Specifically, Transformers is arguably the most successful solution to extract the semantic correlations among the elements in a long sequence. However, in time ...
LTSF-Linear outperforms all transformer-based methods by a large margin. Efficiency. image Comparison of method efficiency with Look-back window size 96 and ...
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Sep 28, 2023 · Transformer-based models can indeed be significantly worse than simple univariate temporal linear models on many commonly used forecasting benchmarks.
Experimental results on nine real-life datasets show that LTSF-Linear surprisingly outperforms existing sophisticated Transformer-based L TSF models in all ...
Duration: 56:33
Posted: Mar 1, 2024
Posted: Mar 1, 2024
Oct 10, 2023 · These forecasters leverage Transformers to model the global dependencies over temporal tokens of time series, with each token formed by multiple ...
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