mlsanityData to 14 Oct 2026

pTSE: A Multi-model Ensemble Method for Probabilistic Time Series Forecasting

arXiv 2305.11304 ↗First posted 16 May 2023cs.LGcited by 16

Not on the map. Why?

Abstract (arXiv)

Various probabilistic time series forecasting models have sprung up and shown remarkably good performance. However, the choice of model highly relies on the characteristics of the input time series and the fixed distribution that the model is based on. Due to the fact that the probability distributions cannot be averaged over different models straightforwardly, the current time series model ensemble methods cannot be directly applied to improve the robustness and accuracy of forecasting. To address this issue, we propose pTSE, a multi-model distribution ensemble method for probabilistic forecasting based on Hidden Markov Model (HMM). pTSE only takes off-the-shelf outputs from member models without requiring further information about each model. Besides, we provide a complete theoretical analysis of pTSE to prove that the empirical distribution of time series subject to an HMM will converge to the stationary distribution almost surely. Experiments on benchmarks show the superiority of pTSE overall member models and competitive ensemble methods.

Key facts. Cited by 16 papers in the map (data through 14 Oct 2026). Builds on 10 of its 55 references that are in the map. First posted on arXiv 16 May 2023.

Builds on

10 of 55 references.

References · 10

  1. 2017DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networkscited by 1,013
  2. 2019Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecastingcited by 761
  3. 2019High-Dimensional Multivariate Forecasting with Low-Rank Gaussian Copula Processescited by 148
  4. 2020Multivariate Probabilistic Time Series Forecasting via Conditioned Normalizing Flowscited by 144
  5. 2018Temporal Pattern Attention for Multivariate Time Series Forecastingcited by 125
  6. 2019Diverse Trajectory Forecasting with Determinantal Point Processescited by 80
  7. 2022Criteria for Classifying Forecasting Methodscited by 48
  8. 2020Ensembles of Localised Models for Time Series Forecastingcited by 20
  9. 2020If You Like It, GAN It. Probabilistic Multivariate Times Series Forecast With GANcited by 14
  10. 2023Continual Causal Inference with Incremental Observational Datacited by 13

Built on by

Cited by year

Citing papers per year: 2023: 7; 2024: 6; 2025: 3.

16 papers.

Sort:

  1. 2023Time-LLM: Time Series Forecasting by Reprogramming Large Language Modelscited by 700
  2. 2023Continual Learning in Predictive Autoscalingcited by 4
  3. 2023Self-supervised Adaptive Pre-training of Multilingual Speech Models for Language and Dialect Identificationcited by 2
  4. 2025Small but Mighty: Enhancing Time Series Forecasting with Lightweight LLMscited by 1
  5. 2025Non-collective Calibrating Strategy for Time Series Forecastingcited by 1
  6. 2023Enhancing Asynchronous Time Series Forecasting with Contrastive Relational Inferencecited by 0
  7. 2023Enhancing Multilingual Speech Recognition through Language Prompt Tuning and Frame-Level Language Adaptercited by 0
  8. 2023GMTR: Graph Matching Transformerscited by 0
  9. 2023DarkShot: Lighting Dark Images with Low-Compute and High-Qualitycited by 0
  10. 2024TRLS: A Time Series Representation Learning Framework via Spectrogram for Medical Signal Processingcited by 0
  11. 2024TEN-GUARD: Tensor Decomposition for Backdoor Attack Detection in Deep Neural Networkscited by 0
  12. 2024T-Pixel2Mesh: Combining Global and Local Transformer for 3D Mesh Generation from a Single Imagecited by 0
4 more
  1. 2024Strategic Arms with Side Communication Prevail Over Low-Regret MAB Algorithmscited by 0
  2. 2024Towards General Deepfake Detection with Dynamic Curriculumcited by 0
  3. 2024Attr-Int: A Simple and Effective Entity Alignment Framework for Heterogeneous Knowledge Graphscited by 0
  4. 2025Mask6D: Masked Pose Priors For 6D Object Pose Estimationcited by 0