![[2502.09683] Channel Dependence, Limited Lookback Windows, and the Simplicity of Datasets: How Biased is Time Series Forecasting?](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
[2502.09683] Channel Dependence, Limited Lookback Windows, and the Simplicity of Datasets: How Biased is Time Series Forecasting?
Summary
This article examines the biases in time series forecasting (TSF) due to arbitrary lookback windows and channel dependence, advocating for tailored hyperparameter tuning to enhance model evaluation accuracy.
Why It Matters
Understanding the biases in time series forecasting is crucial for researchers and practitioners in machine learning. This study highlights the importance of tuning lookback windows and choosing appropriate model architectures, which can significantly impact forecasting performance and research validity.
Key Takeaways
- Lookback windows must be tuned per task to ensure fair model comparisons.
- Channel-Independent models may appear superior due to dataset simplicity, not inherent performance.
- Multivariate models outperform univariate ones in datasets with strong cross-channel dependencies.
- Statistical analysis can guide the choice between Channel-Independent and Channel-Dependent architectures.
- Recommendations for TSF research include careful consideration of hyperparameters and dataset characteristics.
Computer Science > Machine Learning arXiv:2502.09683 (cs) [Submitted on 13 Feb 2025 (v1), last revised 18 Feb 2026 (this version, v3)] Title:Channel Dependence, Limited Lookback Windows, and the Simplicity of Datasets: How Biased is Time Series Forecasting? Authors:Ibram Abdelmalak, Kiran Madhusudhanan, Jungmin Choi, Christian Kloetergens, Vijaya Krishna Yalavarit, Maximilian Stubbemann, Lars Schmidt-Thieme View a PDF of the paper titled Channel Dependence, Limited Lookback Windows, and the Simplicity of Datasets: How Biased is Time Series Forecasting?, by Ibram Abdelmalak and 6 other authors View PDF HTML (experimental) Abstract:In Long-term Time Series Forecasting (LTSF), the lookback window is a critical hyperparameter often set arbitrarily, undermining the validity of model evaluations. We argue that the lookback window must be tuned on a per-task basis to ensure fair comparisons. Our empirical results show that failing to do so can invert performance rankings, particularly when comparing univariate and multivariate methods. Experiments on standard benchmarks reposition Channel-Independent (CI) models, such as PatchTST, as state-of-the-art methods. However, we reveal this superior performance is largely an artifact of weak inter-channel correlations and simplicity of patterns within these specific datasets. Using Granger causality analysis and ODE datasets (with implicit channel correlations), we demonstrate that the true strength of multivariate Channel-Dependent (CD)...
