Long-term Forecasting with TiDE: Time-series Dense Encoder

In recent work, it has been shown that simple linear models can outperform Transformer-based approaches in long-term time-series forecasting. To address the limitations of linear models in modeling non-linear dependencies and covariates, the authors propose a Multi-layer Perceptron (MLP) based encoder-decoder model called Time-series Dense Encoder (TiDE). TiDE combines the simplicity and speed of linear models with the ability to handle covariates and non-linear dependencies. The authors prove theoretically that the simplest linear analogue of their model can achieve near optimal error rates for linear dynamical systems (LDS) under certain assumptions. They also empirically demonstrate that their method can match or outperform previous approaches on popular long-term time-series forecasting benchmarks while being 5-10 times faster than the best Transformer-based model. In terms of related work, prior research has focused on deep neural network models for long-term forecasting such as LongTrans, Informer, Autoformer, FEDFormer and Pyraformer which employ different mechanisms to capture temporal dependencies. Recently, DLinear and PatchTST have shown promising results by leveraging linear mappings and self-attention mechanisms. The problem setting involves long-term time series forecasting benchmarks where the goal is to predict future values based on historical data. The authors propose TiDE as a solution that encodes past time series data along with covariates using dense MLPs and decodes future time series data with covariates using dense MLPs. Overall, the proposed TiDE model offers a simple yet effective deep learning architecture for long term time series forecasting which achieves superior performance compared to existing neural network models while maintaining computational efficiency.

Achieving Optimal Performance for Long-Term Time Series Forecasting with TiDE

Time series forecasting is an important task in many areas of machine learning, from predicting stock prices to forecasting weather. In recent years, deep learning models have been used extensively to tackle this problem. However, these models are often computationally expensive and can be difficult to train due to the long-term dependencies that need to be captured. To address these issues, researchers have proposed simpler linear models which can achieve near optimal performance on certain tasks while being much faster than their deep learning counterparts.

In this paper, the authors propose a Multi-layer Perceptron (MLP) based encoder-decoder model called Time-series Dense Encoder (TiDE), which combines the simplicity and speed of linear models with the ability to handle covariates and non-linear dependencies. The authors prove theoretically that the simplest linear analogue of their model can achieve near optimal error rates for linear dynamical systems (LDS) under certain assumptions. They also empirically demonstrate that their method can match or outperform previous approaches on popular long-term time series forecasting benchmarks while being 5–10 times faster than the best Transformer-based model.

Background

The problem setting involves long term time series forecasting benchmarks where the goal is to predict future values based on historical data. Prior research has focused on deep neural network models for long term forecasting such as LongTrans, Informer, Autoformer, FEDFormer and Pyraformer which employ different mechanisms to capture temporal dependencies. Recently, DLinear and PatchTST have shown promising results by leveraging linear mappings and self attention mechanisms.

Proposed Model: TiDE

The authors propose TiDE as a solution that encodes past time series data along with covariates using dense MLPs and decodes future time series data with covariates using dense MLPs as well. Specifically, they use two separate MLP networks – one for encoding past data points into a latent space representation; another for decoding future predictions from this latent space representation – connected via an attention mechanism which allows them to leverage both temporal information from past observations as well as external features such as seasonality or holidays when making predictions about future values in a given sequence of events or trends over time .

Experimental Results

The authors empirically demonstrate that their method can match or outperform previous approaches on popular long term time series forecasting benchmarks while being 5–10 times faster than the best Transformer based model tested against it in terms of training speed and inference latency respectively . Additionally , they show that TiDE achieves superior performance compared to existing neural network models when evaluated against standard metrics such as mean absolute error , root mean squared error , etc .

Conclusion

Overall , TiDE offers a simple yet effective deep learning architecture for long term time series forecasting which achieves superior performance compared to existing neural network models while maintaining computational efficiency . This work provides valuable insight into how simple linear methods can be combined with more complex architectures in order to improve accuracy without sacrificing speed or scalability .