How Much Can Time-related Features Enhance Time Series Forecasting? (2412.01557v1)

Abstract: Recent advancements in long-term time series forecasting (LTSF) have primarily focused on capturing cross-time and cross-variate (channel) dependencies within historical data. However, a critical aspect often overlooked by many existing methods is the explicit incorporation of \textbf{time-related features} (e.g., season, month, day of the week, hour, minute), which are essential components of time series data. The absence of this explicit time-related encoding limits the ability of current models to capture cyclical or seasonal trends and long-term dependencies, especially with limited historical input. To address this gap, we introduce a simple yet highly efficient module designed to encode time-related features, Time Stamp Forecaster (TimeSter), thereby enhancing the backbone's forecasting performance. By integrating TimeSter with a linear backbone, our model, TimeLinear, significantly improves the performance of a single linear projector, reducing MSE by an average of 23\% on benchmark datasets such as Electricity and Traffic. Notably, TimeLinear achieves these gains while maintaining exceptional computational efficiency, delivering results that are on par with or exceed state-of-the-art models, despite using a fraction of the parameters.

• The paper introduces TimeSter and TimeLinear, a novel approach that explicitly incorporates timestamp-based, time-related features to improve long-term time series forecasting.
• By focusing on temporal context, TimeLinear achieves significant performance improvements, including a 23% MSE reduction on benchmarks, while maintaining high computational efficiency.
• This study highlights the critical role of timestamps for understanding temporal dynamics, offering an efficient and accurate model paradigm suitable for diverse real-world applications.

Insights into Time-related Features for Time Series Forecasting

The paper conducted by Zeng et al. addresses an often-overlooked aspect in the field of long-term time series forecasting (LTSF): the explicit incorporation of time-related features. Traditional LTSF methods prioritize modeling cross-time and cross-variate dependencies within historical data, yet they generally neglect the temporal context denoted by timestamps. This omission limits the predictive capacity of models, especially in capturing cyclical and seasonal trends over extended periods.

Methodological Contributions

This paper introduces a novel approach through the Time Stamp Forecaster (TimeSter), a streamlined module aimed at enhancing the forecasting performance by explicitly encoding time-related features. The proposed model paradigm, TimeLinear, integrates TimeSter with a simple linear backbone, demonstrating substantial improvements in predictive accuracy and efficiency.

1. Time-related Feature Encoding: TimeSter is designed to take timestamp information as input, enhancing the model's ability to predict long-term dependencies in a dataset by utilizing the inherent temporal context. Each timestamp, associated with specific time-related features, such as season or day of the week, can significantly influence variable observations in time series.
2. Model Efficiency and Performance: By focusing on the predictive power of time-related features, TimeLinear achieves a notable 23% reduction in Mean Squared Error (MSE) on benchmarks like Electricity and Traffic datasets, outperforming models reliant solely on linear projection without a considerable increase in computational expenses.
3. Architectural Design: TimeLinear orchestrates a blend of historical observations with temporal features, leading to more robust predictions across diverse domains, including transportation, finance, and climate. The model demonstrates stellar results with drastically fewer computational resources compared to state-of-the-art models.

Implications and Future Directions

The implications of this paper are profound, both theoretically and practically. Theoretically, it underscores the importance of timestamps in understanding and modeling the underlying patterns and temporal dynamics of time series data. Practically, the model's efficiency and accuracy make it well-suited for deployment in real-world applications where computational efficiency is crucial, such as real-time traffic prediction or energy consumption forecasting.

The exploration of time-related features opens several promising avenues for future research. Addressing the challenge of modeling interactions between different time series variables while integrating time-based features could lead to more sophisticated prediction models. Additionally, exploring the impact of increased historical input data on these models may reveal scalability phenomena, further enhancing time series forecasting capabilities.

In conclusion, the methodological advancements presented by Zeng et al. pave the way for more nuanced exploration of temporal dimensions in time series data, offering a substantial leap towards harnessing the full potential of timestamps in predictive modeling.