AdaRNN: Adaptive Learning and Forecasting of Time Series (2108.04443v2)

Abstract: Time series has wide applications in the real world and is known to be difficult to forecast. Since its statistical properties change over time, its distribution also changes temporally, which will cause severe distribution shift problem to existing methods. However, it remains unexplored to model the time series in the distribution perspective. In this paper, we term this as Temporal Covariate Shift (TCS). This paper proposes Adaptive RNNs (AdaRNN) to tackle the TCS problem by building an adaptive model that generalizes well on the unseen test data. AdaRNN is sequentially composed of two novel algorithms. First, we propose Temporal Distribution Characterization to better characterize the distribution information in the TS. Second, we propose Temporal Distribution Matching to reduce the distribution mismatch in TS to learn the adaptive TS model. AdaRNN is a general framework with flexible distribution distances integrated. Experiments on human activity recognition, air quality prediction, and financial analysis show that AdaRNN outperforms the latest methods by a classification accuracy of 2.6% and significantly reduces the RMSE by 9.0%. We also show that the temporal distribution matching algorithm can be extended in Transformer structure to boost its performance.

• The paper introduces AdaRNN, a novel framework that addresses temporal covariate shift by segmenting time series into distinct distribution periods.
• It employs Temporal Distribution Characterization (TDC) and Temporal Distribution Matching (TDM) to reduce divergence and enhance model robustness.
• Experiments show a 2.6% accuracy gain and a 9.0% error reduction, demonstrating its effectiveness for diverse forecasting applications.

Overview of "AdaRNN: Adaptive Learning and Forecasting for Time Series"

The paper "AdaRNN: Adaptive Learning and Forecasting for Time Series" addresses challenges in time series forecasting, specifically focusing on problems associated with the temporal covariate shift (TCS). TCS manifests when the statistical properties of a time series change over time, causing predictive models to face distribution shifts. The authors identify that this issue has not been adequately explored from a distributional perspective within time series modeling.

Temporal Covariate Shift (TCS)

The paper formalizes the TCS problem, highlighting that while traditional models assume a stable temporal structure, real-world applications often involve non-stationary time series where the marginal distribution of data points changes over time but the conditional distribution remains stable.

Proposed Solution: AdaRNN

To tackle TCS, the authors propose a novel framework called Adaptive Recurrent Neural Networks (AdaRNN). The AdaRNN consists of two principal modules:

1. Temporal Distribution Characterization (TDC):
   • This module aims to understand and segment the time series data into distinct periods characterized by different data distributions. The segmentation is optimized to maximize the diversity of these distributions, thereby supporting robust model training under varying conditions. This characterization helps the model in identifying and leveraging common representations across different periods.
2. Temporal Distribution Matching (TDM):
   • Building on the segmented periods from TDC, the TDM module focuses on matching these period-specific distributions. An RNN-based model is employed, and the approach involves modifying traditional recurrent networks to integrate temporal distribution matching mechanisms. This method helps in reducing distributional divergence while learning adaptive features relevant to each period, enhancing the model’s robustness against TCS. Importantly, the framework accommodates flexible distribution distance measures, such as cosine distance and Maximum Mean Discrepancy (MMD).

Experimental Results

The authors conduct extensive experiments on various datasets, including human activity recognition, air quality prediction, household power consumption, and financial analysis. AdaRNN achieved a 2.6% improvement in accuracy for classification tasks and a 9.0% reduction in mean squared error for regression problems compared to state-of-the-art methods. Moreover, the framework's design is agnostic to the specific architectures of RNNs, such as LSTMs and GRUs, and can be extended to non-recurrent models like Transformers to further enhance performance.

Implications and Future Directions

The implications of this work are significant for domains reliant on time series forecasting, including finance, healthcare, and environmental science. The ability to model adaptive time series predictions through the lens of distribution shifts enhances predictive accuracy across fluctuating temporal datasets.

Looking forward, the paper opens several avenues for further research. Extensions could include integrating more sophisticated distribution matching techniques, exploring different neural architectures beyond RNNs, and applying the AdaRNN framework to online and streaming data scenarios. Furthermore, an end-to-end approach that combines both TDC and TDM in a unified learning framework could offer advantages in terms of computational efficiency and model robustness. These improvements could lead to more generalized time series models that continuously adapt to temporal dynamics in real-time applications.