Spatial-Temporal Identity: A Simple yet Effective Baseline for Multivariate Time Series Forecasting (2208.05233v2)

Abstract: Multivariate Time Series (MTS) forecasting plays a vital role in a wide range of applications. Recently, Spatial-Temporal Graph Neural Networks (STGNNs) have become increasingly popular MTS forecasting methods due to their state-of-the-art performance. However, recent works are becoming more sophisticated with limited performance improvements. This phenomenon motivates us to explore the critical factors of MTS forecasting and design a model that is as powerful as STGNNs, but more concise and efficient. In this paper, we identify the indistinguishability of samples in both spatial and temporal dimensions as a key bottleneck, and propose a simple yet effective baseline for MTS forecasting by attaching Spatial and Temporal IDentity information (STID), which achieves the best performance and efficiency simultaneously based on simple Multi-Layer Perceptrons (MLPs). These results suggest that we can design efficient and effective models as long as they solve the indistinguishability of samples, without being limited to STGNNs.

• The paper introduces Spatial-Temporal Identity (STID), a simple model leveraging MLPs and spatial/temporal identity embeddings for multivariate time series forecasting.
• STID achieves superior accuracy and efficiency on multiple benchmark datasets compared to complex state-of-the-art graph neural network models.
• The findings indicate that effective models can be developed by addressing sample indistinguishability, enabling scalability for real-time applications like traffic and energy forecasting.

A Simple, Efficient Model for Multivariate Time Series Forecasting

Multivariate Time Series Forecasting (MTS) is a critical challenge in fields like transportation and energy management, where accurate predictions can significantly impact decision-making processes. The paper "Spatial-Temporal Identity: A Simple yet Effective Baseline for Multivariate Time Series Forecasting" proposes a fresh approach by simplifying the model architecture to enhance both efficacy and efficiency in MTS forecasting, outperforming more complex existing methods.

Analysis of Current Models

Most advanced MTS forecasting models hinge on Spatial-Temporal Graph Neural Networks (STGNNs), which utilize Graph Convolution Networks (GCNs) to process spatial dependencies and sequential models, such as RNNs, to analyze temporal patterns. Despite their effectiveness, these models are increasingly sophisticated, exhibiting only incremental improvements. The authors posit that addressing the indistinguishability of samples in spatial and temporal dimensions could obviate the need for complex architectures like STGNNs.

Introducing Spatial-Temporal Identity (STID)

The paper presents the Spatial-Temporal Identity (STID) model, leveraging simple Multi-Layer Perceptrons (MLPs) enriched with spatial and temporal identity information. The proposed method encodes MTS data with three embedding matrices: a spatial embedding matrix and two temporal embedding matrices representing time slots and days of the week. This innovative approach directly targets the indistinguishable sample problem by embedding identity attributes that differentiate between samples with similar historical data but potentially divergent future predictions.

Model Architecture and Implementation

STID's architecture is streamlined, consisting of:

• Embedding Layer: Converts raw data into latent representations using spatial and temporal embeddings.
• MLP Layers: Several layers equipped with residual connections to encode the embedded data.
• Regression Layer: Outputs final predictions through a simple MLP.

The results across multiple datasets, including PEMS04, PEMS07, PEMS08, PEMS-BAY, and Electricity, indicate that STID consistently yields superior accuracy compared to traditional models and recent STGNN approaches. The simplicity of STID not only enhances computational efficiency but also ultimately achieves notable predictive performance.

Implications and Future Prospects

The findings suggest that efficient models can be developed by neatly addressing the indistinguishability issue without over-relying on costly graph operations. This approach has substantial implications for applications involving large-scale, complex time-series data. The reduced computational overhead fosters scalability, making STID suitable for real-time applications in traffic predictions, energy demand forecasts, and financial market analysis.

Future research could focus on further enhancing the temporal embedding to capture more complex periodic behaviors or integrating external covariates for enriched context-awareness. Additionally, the exploration of alternative identity embedding techniques could reveal novel opportunities for interpreting temporal data within dynamic systems, paving the way for broader applications in AI-driven forecasting.

In conclusion, "Spatial-Temporal Identity: A Simple yet Effective Baseline for Multivariate Time Series Forecasting" exemplifies how innovation through simplification can lead to practical, yet scientifically robust models. This work challenges the prevailing reliance on complexity in deep learning architectures and opens a dialogue on efficiency-driven design in AI forecasting methods.