TimeES: Probabilistic and Deterministic Time Series Forecasting via Evolutionary Spectra

arXiv:2609.32384 · cs.LG, cs.AI · Submitted 2026-09-26 · Read on arXiv

cs.LG, cs.AI

Submitted: 2026-09-26

Updated: 2026-09-26

Code: https://github.com/wwy155/TimeES

Terminology

• Time Series Forecasting — 32× in this paper · explained in 3 episodes
• Probabilistic Forecasting — 26× in this paper · explained in 3 episodes
• Non-stationarity — 14× in this paper · explained in 2 episodes
• Transformer — 14× in this paper · explained in 2 episodes
• Benchmark — 12× in this paper · explained in 2 episodes
• Accuracy — 11× in this paper · explained in 1 episode
• Generative Model — 9× in this paper · explained in 2 episodes
• Core — 8× in this paper · explained in 2 episodes
• Diffusion Model — 8× in this paper · explained in 8 episodes
• Diffusion Models — 8× in this paper · explained in 6 episodes
• Fourier Transform — 8× in this paper
• Sparsity — 7× in this paper · explained in 4 episodes
• Ablation — 6× in this paper · explained in 1 episode
• Interpretability — 6× in this paper · explained in 15 episodes
• Uncertainty Quantification — 6× in this paper · explained in 8 episodes
• Discrete Evolutionary Spectra — 4× in this paper
• Dynamic Mode Decomposition — 4× in this paper
• Generative Models — 4× in this paper · explained in 2 episodes
• Representation Learning — 4× in this paper · explained in 2 episodes
• Distribution Shift — 3× in this paper · explained in 6 episodes
• Mean Squared Error — 3× in this paper
• Ablation Studies — 2× in this paper · explained in 1 episode
• Adversarial Training — 2× in this paper · explained in 1 episode
• Calibration — 2× in this paper · explained in 7 episodes
• Downstream Tasks — 2× in this paper · explained in 2 episodes
• Empirical Coverage — 2× in this paper · explained in 2 episodes
• Generative Modeling — 2× in this paper · explained in 3 episodes
• Institutional Review Board — 2× in this paper
• Wavelet Transform — 2× in this paper · explained in 2 episodes
• Ablation study — 1× in this paper · explained in 1 episode
• Adaptivity — 1× in this paper · explained in 1 episode
• Additive Decomposition — 1× in this paper · explained in 1 episode
• Additivity — 1× in this paper · explained in 1 episode
• Aleatoric Uncertainty — 1× in this paper · explained in 5 episodes
• Clustering — 1× in this paper · explained in 2 episodes
• Computational overhead — 1× in this paper · explained in 2 episodes
• Diffusion Process — 1× in this paper · explained in 2 episodes
• Dynamic Mode Decomposition (DMD) — 1× in this paper · explained in 3 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Mean Absolute Error — 1× in this paper
• Motivated by Evolutionary Spectra — 1× in this paper
• Pruning — 1× in this paper · explained in 4 episodes
• Quantile Interval Calibration Error — 1× in this paper
• University of East Anglia — 1× in this paper
• Variational Autoencoder (VAE) — 1× in this paper · explained in 3 episodes

Sources

• The UEA multivariate time series classification archive, 2018
• TimeVAE: A Variational Auto-Encoder for Multivariate Time Series Generation
• C-RNN-GAN: Continuous recurrent neural networks with adversarial training
• Diffusion Models for Time Series Forecasting: A Survey

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