Cover Image

Hyperparameter Tuning MLPs for Probabilistic Time Series Forecasting

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Madhusudhanan, Kiran, Jawed, Shayan, Schmidt-Thieme, Lars
Format:	Preprint
Published:	2024
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Marginalization Consistent Probabilistic Forecasting of Irregular Time Series via Mixture of Separable flows
by: Yalavarthi, Vijaya Krishna, et al.
Published: (2024)

ProbSAINT: Probabilistic Tabular Regression for Used Car Pricing
by: Madhusudhanan, Kiran, et al.
Published: (2024)

Channel Dependence, Limited Lookback Windows, and the Simplicity of Datasets: How Biased is Time Series Forecasting?
by: Abdelmalak, Ibram, et al.
Published: (2025)

Probabilistic Circuits for Irregular Multivariate Time Series Forecasting
by: Klötergens, Christian, et al.
Published: (2026)

TabResFlow: A Normalizing Spline Flow Model for Probabilistic Univariate Tabular Regression
by: Madhusudhanan, Kiran, et al.
Published: (2025)

Probabilistic Forecasting of Irregular Time Series via Conditional Flows
by: Yalavarthi, Vijaya Krishna, et al.
Published: (2024)

HPMixer: Hierarchical Patching for Multivariate Time Series Forecasting
by: Choi, Jung Min, et al.
Published: (2026)

NPMixer: Hierarchical Neighboring Patch Mixing for Time Series Forecasting
by: Choi, Jung Min, et al.
Published: (2026)

Functional Latent Dynamics for Irregularly Sampled Time Series Forecasting
by: Klötergens, Christian, et al.
Published: (2024)

Wave-Mask/Mix: Exploring Wavelet-Based Augmentations for Time Series Forecasting
by: Arabi, Dona, et al.
Published: (2024)

Temporal Patch Shuffle (TPS): Leveraging Patch-Level Shuffling to Boost Generalization and Robustness in Time Series Forecasting
by: Bakhshaliyev, Jafar, et al.
Published: (2026)

Mixing It Up: Exploring Mixer Networks for Irregular Multivariate Time Series Forecasting
by: Klötergens, Christian, et al.
Published: (2025)

Why Attention Fails: The Degeneration of Transformers into MLPs in Time Series Forecasting
by: Liang, Zida, et al.
Published: (2025)

Valid and Expressive Copulas for Irregular Multivariate Time Series
by: Klötergens, Christian, et al.
Published: (2026)

Physiome-ODE: A Benchmark for Irregularly Sampled Multivariate Time Series Forecasting Based on Biological ODEs
by: Klötergens, Christian, et al.
Published: (2025)

Boosting MLPs with a Coarsening Strategy for Long-Term Time Series Forecasting
by: Bian, Nannan, et al.
Published: (2024)

Tripletformer for Probabilistic Interpolation of Irregularly sampled Time Series
by: Yalavarthi, Vijaya Krishna, et al.
Published: (2022)

Bayesian Active Learning By Distribution Disagreement
by: Werner, Thorben, et al.
Published: (2025)

Echo State Networks for Time Series Forecasting: Hyperparameter Sweep and Benchmarking
by: Häußer, Alexander
Published: (2026)

MDMLP-EIA: Multi-domain Dynamic MLPs with Energy Invariant Attention for Time Series Forecasting
by: Zhang, Hu, et al.
Published: (2025)

Are EEG Sequences Time Series? EEG Classification with Time Series Models and Joint Subject Training
by: Burchert, Johannes, et al.
Published: (2024)

Koopman Ensembles for Probabilistic Time Series Forecasting
by: Frion, Anthony, et al.
Published: (2024)

A Meta-Knowledge-Augmented LLM Framework for Hyperparameter Optimization in Time-Series Forecasting
by: Saadallah, Ons, et al.
Published: (2026)

Multivariate Probabilistic Time Series Forecasting with Correlated Errors
by: Zheng, Vincent Zhihao, et al.
Published: (2024)

Probabilistic Multivariate Time Series Forecasting with Diffusion Copulas
by: Huk, David, et al.
Published: (2026)

Better Batch for Deep Probabilistic Time Series Forecasting
by: Zheng, Vincent Zhihao, et al.
Published: (2023)

Towards Comparable Active Learning
by: Werner, Thorben, et al.
Published: (2023)

Recurrent State Encoders for Efficient Neural Combinatorial Optimization
by: Dernedde, Tim, et al.
Published: (2025)

A Unified Hyperparameter Optimization Pipeline for Transformer-Based Time Series Forecasting Models
by: Xu, Jingjing, et al.
Published: (2025)

Conditionally Whitened Generative Models for Probabilistic Time Series Forecasting
by: Yang, Yanfeng, et al.
Published: (2025)

Causally-Constrained Probabilistic Forecasting for Time-Series Anomaly Detection
by: Khosravinia, Pooyan, et al.
Published: (2026)

Non-stationary Diffusion For Probabilistic Time Series Forecasting
by: Ye, Weiwei, et al.
Published: (2025)

ProbRes: Volatility Learning for Probabilistic Time-Series Forecasting
by: Wang, Tingting, et al.
Published: (2026)

The Role of Active Learning in Modern Machine Learning
by: Werner, Thorben, et al.
Published: (2025)

HMAR: Hierarchical Masked Attention for Multi-Behaviour Recommendation
by: Elsayed, Shereen, et al.
Published: (2024)

Training-Free Probabilistic Time-Series Forecasting with Conformal Seasonal Pools
by: Manokhin, Valery
Published: (2026)

Beyond MSE: Ordinal Cross-Entropy for Probabilistic Time Series Forecasting
by: Wang, Jieting, et al.
Published: (2025)

Noise Titration: Exact Distributional Benchmarking for Probabilistic Time Series Forecasting
by: Wang, Qilin
Published: (2026)

U-Former ODE: Fast Probabilistic Forecasting of Irregular Time Series
by: Kuleshov, Ilya, et al.
Published: (2026)

Benchmarking Probabilistic Time Series Forecasting Models on Neural Activity
by: Lu, Ziyu, et al.
Published: (2025)