An Accurate and Fully-Automated Ensemble Model for Weekly Time Series Forecasting

• URL: http://arxiv.org/abs/2010.08158v2
• Date: Sun, 3 Dec 2023 23:41:04 GMT
• Title: An Accurate and Fully-Automated Ensemble Model for Weekly Time Series Forecasting
• Authors: Rakshitha Godahewa, Christoph Bergmeir, Geoffrey I. Webb, Pablo Montero-Manso
• Abstract summary: We propose a forecasting method in this domain, leveraging state-of-the-art forecasting techniques. We consider different meta-learning architectures, algorithms, and base model pools. Our proposed method consistently outperforms a set of benchmarks and state-of-the-art weekly forecasting models.
• Score: 9.617563440471928
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Many businesses and industries require accurate forecasts for weekly time series nowadays. However, the forecasting literature does not currently provide easy-to-use, automatic, reproducible and accurate approaches dedicated to this task. We propose a forecasting method in this domain to fill this gap, leveraging state-of-the-art forecasting techniques, such as forecast combination, meta-learning, and global modelling. We consider different meta-learning architectures, algorithms, and base model pools. Based on all considered model variants, we propose to use a stacking approach with lasso regression which optimally combines the forecasts of four base models: a global Recurrent Neural Network model (RNN), Theta, Trigonometric Box-Cox ARMA Trend Seasonal (TBATS) and Dynamic Harmonic Regression ARIMA (DHR-ARIMA), as it shows the overall best performance across seven experimental weekly datasets on four evaluation metrics. Our proposed method also consistently outperforms a set of benchmarks and state-of-the-art weekly forecasting models by a considerable margin with statistical significance. Our method can produce the most accurate forecasts, in terms of mean sMAPE, for the M4 weekly dataset among all benchmarks and all original competition participants.

Related papers

• GIFT-Eval: A Benchmark For General Time Series Forecasting Model Evaluation [90.53485251837235]
  Time series foundation models excel in zero-shot forecasting, handling diverse tasks without explicit training. GIFT-Eval is a pioneering benchmark aimed at promoting evaluation across diverse datasets. GIFT-Eval encompasses 23 datasets over 144,000 time series and 177 million data points.
  arXiv  Detail & Related papers  (2024-10-14T11:29:38Z)
• Learning Augmentation Policies from A Model Zoo for Time Series Forecasting [58.66211334969299]
  We introduce AutoTSAug, a learnable data augmentation method based on reinforcement learning. By augmenting the marginal samples with a learnable policy, AutoTSAug substantially improves forecasting performance.
  arXiv  Detail & Related papers  (2024-09-10T07:34:19Z)
• An ensemble of data-driven weather prediction models for operational sub-seasonal forecasting [0.08106028186803123]
  We present an operations-ready multi-model ensemble weather forecasting system. It is possible to achieve near-state-of-the-art subseasonal-to-seasonal forecasts using a multi-model ensembling approach with data-driven weather prediction models.
  arXiv  Detail & Related papers  (2024-03-22T20:01:53Z)
• Recency-Weighted Temporally-Segmented Ensemble for Time-Series Modeling [0.0]
  Time-series modeling in process industries faces the challenge of dealing with complex, multi-faceted, and evolving data characteristics. We introduce the Recency-Weighted Temporally-Segmented (ReWTS) ensemble model, a novel chunk-based approach for multi-step forecasting. We present a comparative analysis, utilizing two years of data from a wastewater treatment plant and a drinking water treatment plant in Norway.
  arXiv  Detail & Related papers  (2024-03-04T16:00:35Z)
• Predictive Churn with the Set of Good Models [64.05949860750235]
  We study the effect of conflicting predictions over the set of near-optimal machine learning models. We present theoretical results on the expected churn between models within the Rashomon set. We show how our approach can be used to better anticipate, reduce, and avoid churn in consumer-facing applications.
  arXiv  Detail & Related papers  (2024-02-12T16:15:25Z)
• FengWu-4DVar: Coupling the Data-driven Weather Forecasting Model with 4D Variational Assimilation [67.20588721130623]
  We develop an AI-based cyclic weather forecasting system, FengWu-4DVar. FengWu-4DVar can incorporate observational data into the data-driven weather forecasting model. Experiments on the simulated observational dataset demonstrate that FengWu-4DVar is capable of generating reasonable analysis fields.
  arXiv  Detail & Related papers  (2023-12-16T02:07:56Z)
• Attention-Based Ensemble Pooling for Time Series Forecasting [55.2480439325792]
  We propose a method for pooling that performs a weighted average over candidate model forecasts. We test this method on two time-series forecasting problems: multi-step forecasting of the dynamics of the non-stationary Lorenz 63 equation, and one-step forecasting of the weekly incident deaths due to COVID-19.
  arXiv  Detail & Related papers  (2023-10-24T22:59:56Z)
• Beyond Ensemble Averages: Leveraging Climate Model Ensembles for Subseasonal Forecasting [10.083361616081874]
  This study explores an application of machine learning (ML) models as post-processing tools for subseasonal forecasting. Lagged numerical ensemble forecasts and observational data, including relative humidity, pressure at sea level, and geopotential height, are incorporated into various ML methods. For regression, quantile regression, and tercile classification tasks, we consider using linear models, random forests, convolutional neural networks, and stacked models.
  arXiv  Detail & Related papers  (2022-11-29T01:11:04Z)
• Evaluating State of the Art, Forecasting Ensembles- and Meta-learning Strategies for Model Fusion [0.0]
  This paper focuses on the utility of the Exponential-Smoothing-Recurrent Neural Network (ES-RNN) in the pool of base models for different ensembles.
  arXiv  Detail & Related papers  (2022-03-07T10:51:40Z)
• Back2Future: Leveraging Backfill Dynamics for Improving Real-time Predictions in Future [73.03458424369657]
  In real-time forecasting in public health, data collection is a non-trivial and demanding task. 'Backfill' phenomenon and its effect on model performance has been barely studied in the prior literature. We formulate a novel problem and neural framework Back2Future that aims to refine a given model's predictions in real-time.
  arXiv  Detail & Related papers  (2021-06-08T14:48:20Z)
• Two-Step Meta-Learning for Time-Series Forecasting Ensemble [1.1278903078792915]
  forecasting using an ensemble of several methods is often seen as a compromise. We propose to predict these aspects adaptively using meta-learning. The proposed approach was tested on 12561 micro-economic time-series.
  arXiv  Detail & Related papers  (2020-11-20T18:35:02Z)

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.