README.md

Supervised Forecasting Models Benchmarking

• Supervised Forecasting Models Benchmarking
  • Experimental Results Reproduction
  • Key Insights & Takeaways
    • Point vs. Probabilistic Estimation
    • Autoregressive vs. Non-autoregressive Decoding Scheme
    • Instance-level Normalization Choice
  • Experimental Result Details

Experimental Results Reproduction

Reproduce the experimental results using the provided scripts:

• Long-Term Forecasting:

bash scripts/reproduce_ltsf_results.sh

Configuration files: config/ltsf/.

• Short-Term Forecasting:

bash scripts/reproduce_stsf_results.sh

Configuration files: config/stsf/.

Key Insights & Takeaways

Point vs. Probabilistic Estimation

Insights

• Current supervised long-term point forecasting models (e.g., DLinear, PatchTST, iTransformer) struggle with intricate data distributions.
• Current supervised short-term probabilistic forecasting models (e.g., GRU NVP, TimeGrad, CSDI) face challenges in extended forecasting horizons.

Takeaways

• It is important to consider both long-term and short-term evaluation scenarios.
• Leverage both point and distributional metrics for more comprehensive insights.

Autoregressive vs. Non-autoregressive Decoding Scheme

Insights

• Current Supervised Non-Autoregressive (NAR) Models (e.g., PatchTST, iTransformer, CSDI)
  • Primarily developed for long-term forecasting scenarios.
  • Suboptimal for short-term forecasting, and some models are memory-intensive.
• Current Supervised Autoregressive (AR) Models (e.g., GRU, GRU NVP, TimeGrad)
  • Primarily developed for short-term forecasting scenarios
  • Perform well with strong seasonality but struggle with long-term, strong trends

Takeaways

• It is crucial to select the right methodological design based on the specific data characteristics.
• There are tremendous re-design opportunities, given the comprehensive forecasting needs.

Instance-level Normalization Choice

Insights

• Reversible Instance Normalization (RevIN): Essential for Long-term Forecasting Scenarios
  • Our observation: AR models in the literature are scarce for long-term forecasting
  • Our finding: RevIN + AR => A simple yet highly effective baseline that has been overlooked
• Normalization Choices under Short-term Forecasting Scenarios
  • No dominating normalization strategies

Takeaways

• The co-design of normalization techniques and model architectures warrants further research attention.
• The challenges and opportunities in time-series normalization persist in balancing short-term and long-term forecasting needs.

Experimental Result Details

Long-Term Forecasting Benchmarking

Table 1. Results ($\textrm{mean}_{\textrm{std}}$) on long-term forecasting scenarios with the best in $\textbf{bold}$ and the second $\underline{\textrm{underlined}}$, each containing five independent runs with different seeds. The input sequence length is set to 36 for the ILI-L dataset and 96 for the others. Due to the excessive time and memory consumption of CSDI in producing long-term forecasts, its results are unavailable in some datasets.

Short-Term Forecasting Benchmarking

Table 2.Results ($\textrm{mean}_{\textrm{std}}$) on short-term forecasting scenarios with the best in $\textbf{bold}$ and the second $\underline{\textrm{underlined}}$, each containing five independent runs with different seeds.