Multi-layer Stack Ensembles for Time Series Forecasting

• URL: http://arxiv.org/abs/2511.15350v1
• Date: Wed, 19 Nov 2025 11:21:00 GMT
• Title: Multi-layer Stack Ensembles for Time Series Forecasting
• Authors: Nathanael Bosch, Oleksandr Shchur, Nick Erickson, Michael Bohlke-Schneider, Caner Türkmen,
• Abstract summary: Ensembling is a powerful technique for improving the accuracy of machine learning models.<n>We evaluate 33 ensemble models across 50 real-world datasets.<n>We propose a multi-layer stacking framework for time series forecasting.
• Score: 20.643484889051425
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Ensembling is a powerful technique for improving the accuracy of machine learning models, with methods like stacking achieving strong results in tabular tasks. In time series forecasting, however, ensemble methods remain underutilized, with simple linear combinations still considered state-of-the-art. In this paper, we systematically explore ensembling strategies for time series forecasting. We evaluate 33 ensemble models -- both existing and novel -- across 50 real-world datasets. Our results show that stacking consistently improves accuracy, though no single stacker performs best across all tasks. To address this, we propose a multi-layer stacking framework for time series forecasting, an approach that combines the strengths of different stacker models. We demonstrate that this method consistently provides superior accuracy across diverse forecasting scenarios. Our findings highlight the potential of stacking-based methods to improve AutoML systems for time series forecasting.

Related papers

• When Does Multimodality Lead to Better Time Series Forecasting? [96.26052272121615]
  We investigate whether and under what conditions such multimodal integration consistently yields gains.<n>Our findings reveal that the benefits of multimodality are highly condition-dependent.<n>Our study offers a rigorous, quantitative foundation for understanding when multimodality can be expected to aid forecasting tasks.
  arXiv  Detail & Related papers  (2025-06-20T23:55:56Z)
• Breaking Silos: Adaptive Model Fusion Unlocks Better Time Series Forecasting [64.45587649141842]
  Time-series forecasting plays a critical role in many real-world applications.<n>No single model consistently outperforms others across different test samples, but instead (ii) each model excels in specific cases.<n>We introduce TimeFuse, a framework for collective time-series forecasting with sample-level adaptive fusion of heterogeneous models.
  arXiv  Detail & Related papers  (2025-05-24T00:45:07Z)
• Scalable Permutation-Aware Modeling for Temporal Set Prediction [8.122126170969365]
  Temporal set prediction involves forecasting the elements that will appear in the next set, given a sequence of prior sets.<n>Existing methods often rely on intricate architectures with substantial computational overhead.<n>We introduce a novel and scalable framework that leverages permutation-equivariant and permutation-invariant transformations.
  arXiv  Detail & Related papers  (2025-04-23T23:14:35Z)
• EnsIR: An Ensemble Algorithm for Image Restoration via Gaussian Mixture Models [70.60381055741391]
  Image restoration challenges related to illposed problems, resulting in deviations between single model predictions and ground-truths. Ensemble learning aims to address these deviations by combining the predictions of multiple base models. We employ an expectation (EM)-based algorithm to estimate ensemble weights for prediction candidates. Our algorithm is model-agnostic and training-free, allowing seamless integration and enhancement of various pre-trained image restoration models.
  arXiv  Detail & Related papers  (2024-10-30T12:16:35Z)
• Can time series forecasting be automated? A benchmark and analysis [4.19475889117731]
  Time series forecasting plays a pivotal role across various domains such as finance, healthcare, and weather. The task of selecting the most suitable forecasting method for a given dataset is a complex task due to the diversity of data patterns and characteristics. This research proposes a comprehensive benchmark for evaluating and ranking time series forecasting methods across a wide range of datasets.
  arXiv  Detail & Related papers  (2024-07-23T12:54:06Z)
• LoRA-Ensemble: Efficient Uncertainty Modelling for Self-Attention Networks [52.46420522934253]
  We introduce LoRA-Ensemble, a parameter-efficient ensembling method for self-attention networks.<n>The method not only outperforms state-of-the-art implicit techniques like BatchEnsemble, but even matches or exceeds the accuracy of an Explicit Ensemble.
  arXiv  Detail & Related papers  (2024-05-23T11:10:32Z)
• Chronos: Learning the Language of Time Series [79.38691251254173]
  Chronos is a framework for pretrained probabilistic time series models. We show that Chronos models can leverage time series data from diverse domains to improve zero-shot accuracy on unseen forecasting tasks.
  arXiv  Detail & Related papers  (2024-03-12T16:53:54Z)
• Cluster-and-Conquer: A Framework For Time-Series Forecasting [94.63501563413725]
  We propose a three-stage framework for forecasting high-dimensional time-series data. Our framework is highly general, allowing for any time-series forecasting and clustering method to be used in each step. When instantiated with simple linear autoregressive models, we are able to achieve state-of-the-art results on several benchmark datasets.
  arXiv  Detail & Related papers  (2021-10-26T20:41:19Z)
• Ensembles of Randomized NNs for Pattern-based Time Series Forecasting [0.0]
  We propose an ensemble forecasting approach based on randomized neural networks. A pattern-based representation of time series makes the proposed approach suitable for forecasting time series with multiple seasonality. Case studies conducted on four real-world forecasting problems verified the effectiveness and superior performance of the proposed ensemble forecasting approach.
  arXiv  Detail & Related papers  (2021-07-08T20:13:50Z)
• StackGenVis: Alignment of Data, Algorithms, and Models for Stacking Ensemble Learning Using Performance Metrics [4.237343083490243]
  In machine learning (ML), ensemble methods such as bagging, boosting, and stacking are widely-established approaches. StackGenVis is a visual analytics system for stacked generalization.
  arXiv  Detail & Related papers  (2020-05-04T15:43:55Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.