Fast and Efficient Long Time Series Forecasting with Residual Cyclic Transformers

Core Concepts

ReCycle, a method for reducing runtime and energy consumption in long time series forecasting with Transformer-based architectures, introduces primary cycle compression and learning residuals to address the computational complexity of the attention mechanism and improve prediction accuracy.

Abstract

The paper presents ReCycle, a method for fast and efficient long time series forecasting using Transformer-based architectures. The key contributions are: Primary Cycle Compression (PCC): The authors rearrange the univariate time series data into a 2D matrix based on the primary cycle (e.g., daily) to address the scalar breakdown of dot-product attention in Transformers for single-feature sequences. This reduces the sequence length and computational complexity. Residual Learning: The authors leverage recent historic profiles (RHP) as a baseline and train the model to learn the residuals between the RHP and the actual data. This allows the model to focus on learning the harder-to-predict modulations rather than the predominant periodic patterns. The authors evaluate ReCycle as an extension to three state-of-the-art Transformer-based models (Transformer, FEDformer, PatchTST) on several time series datasets. The results show that ReCycle can significantly improve the forecasting accuracy of these models while drastically reducing their training time and energy consumption, making them more feasible for practical deployment. The authors also demonstrate that with ReCycle, Transformer-based approaches can outperform non-Transformer models like NHiTS.

Stats

The electricity consumption of 370 household consumers in Portugal from 2011 to 2014 ranges from 0 to 6 GW. The total electricity consumption in Germany from 2015 to 2020 ranges from 40 to 60 GW. The water consumption in a regional water supply network in Germany from 2016 to 2021 ranges from 0 to 4000 m^3. The traffic data on Interstate 280 in California from 2016 to 2018 ranges from 50,000 to 70,000 vehicles. The oil temperature of an electricity transformer in China from 2016 to 2018 ranges from 0 to 50 degrees Celsius.

Quotes

"ReCycle utilizes primary cycle compression to address the computational complexity of the attention mechanism in long time series." "By learning residuals from refined smoothing average techniques, ReCycle surpasses state-of-the-art accuracy in a variety of application use cases." "ReCycle reduces the run time and energy consumption by more than an order of magnitude, making both training and inference feasible on low-performance, low-power and edge computing devices."

Key Insights Distilled From

ReCycle: Fast and Efficient Long Time Series Forecasting with Residual Cyclic Transformers

by Arvi... at arxiv.org 05-07-2024

https://arxiv.org/pdf/2405.03429.pdf

Deeper Inquiries

How can the ReCycle approach be extended to handle multivariate time series forecasting?

The ReCycle approach can be extended to handle multivariate time series forecasting by modifying the primary cycle compression (PCC) and residual learning components to accommodate multiple features in the data. Primary Cycle Compression (PCC): Instead of converting univariate time series into multivariate time series over primary cycles, the PCC process can be adjusted to incorporate multiple features at each time step. This would involve restructuring the data matrix to include all relevant features in addition to the primary cycle information. The primary cycle length parameter, D, would need to be adjusted to account for the additional features, ensuring that the model can capture the relationships between different variables over the primary cycle. Residual Learning with Multivariate Data: When learning residuals from recent historic profiles (RHP), the model would need to calculate residuals for each feature in the multivariate time series. This would involve subtracting the RHP for each feature from the original data to capture the deviations specific to each variable. The model architecture would need to be adapted to handle the multidimensional nature of the residuals, ensuring that the network can effectively learn and predict deviations for each feature. By extending ReCycle to handle multivariate time series forecasting, the model can capture more complex relationships and dependencies between multiple variables, leading to more accurate and robust predictions across diverse datasets.

What are the limitations of the RHP-based residual learning approach, and how can it be further improved to handle more complex temporal patterns?

The RHP-based residual learning approach, while effective in capturing general time course patterns and deviations, has some limitations that can be addressed for handling more complex temporal patterns: Limited Temporal Context: RHP only considers a fixed number of past days for averaging, which may not capture long-term dependencies or subtle temporal variations. To improve this, the model can be enhanced to incorporate a more extensive historical context, possibly using attention mechanisms to focus on relevant past data points. Handling Non-Linear Patterns: RHP-based residuals may struggle with non-linear temporal patterns that cannot be effectively captured through simple averaging. Introducing non-linear transformations or more sophisticated feature engineering techniques can help the model adapt to complex temporal dynamics. Concept Drift Adaptation: RHP may not adequately address concept drift, where the underlying data distribution changes over time. Implementing adaptive mechanisms that can detect and adjust to concept drift in real-time can enhance the model's ability to handle evolving temporal patterns. Incorporating Seasonality and Trends: RHP may overlook seasonal trends or long-term patterns that impact the time series data. Including additional features or external factors related to seasonality and trends can provide the model with more context to make accurate predictions. By addressing these limitations through advanced modeling techniques, feature engineering, and adaptive mechanisms, the RHP-based residual learning approach can be further improved to handle a wider range of complex temporal patterns in time series forecasting.

What other types of prior knowledge or domain-specific information could be incorporated into the ReCycle framework to enhance its performance and applicability across a wider range of time series forecasting problems?

To enhance the performance and applicability of the ReCycle framework across a wider range of time series forecasting problems, additional types of prior knowledge and domain-specific information can be incorporated: External Factors: Including external factors such as weather data, economic indicators, or social events that may influence the time series can provide valuable context for the model to make more accurate predictions. These external factors can be encoded as additional features or metadata in the input data. Event Detection: Integrating event detection algorithms to identify significant events or anomalies in the time series data can help the model adapt its predictions accordingly. By incorporating event detection mechanisms, ReCycle can improve its ability to handle sudden changes or irregular patterns in the data. Hierarchical Structures: Leveraging hierarchical structures in the data, such as grouping related time series together or capturing dependencies between different levels of aggregation, can enhance the model's understanding of complex relationships within the data. Hierarchical information can be encoded in the input data to guide the forecasting process. Expert Knowledge Integration: Incorporating domain experts' insights and knowledge into the model training process can provide valuable guidance on relevant features, patterns, or relationships that may not be apparent from the data alone. Expert knowledge can be used to inform model architecture design, feature selection, or hyperparameter tuning. By incorporating a diverse range of prior knowledge and domain-specific information into the ReCycle framework, the model can adapt to various forecasting scenarios, improve prediction accuracy, and handle a wider array of time series data with different characteristics and complexities.