Automatic Forecasting via Meta-Learning

2021-09-29 · Mustafa Abdallah, Ryan Rossi, Kanak Mahadik, Sungchul Kim, Handong Zhao, Haoliang Wang, Saurabh Bagchi

In this work, we develop techniques for fast automatic selection of the best forecasting model for a new unseen time-series dataset, without having to first train (or evaluate) all the models on the new time-series data to select the best one. In particular, we develop a forecasting meta-learning approach called AutoForecast that allows for the quick inference of the best time-series forecasting model for an unseen dataset. Our approach learns both forecasting models performances over time horizon of same dataset and task similarity across different datasets. The experiments demonstrate the effectiveness of the approach over state-of-the-art (SOTA) single and ensemble methods and several SOTA meta-learners (adapted to our problem) in terms of selecting better forecasting models (i.e., 2X gain) for unseen tasks for univariate and multivariate testbeds.

📄 PDF Abstract BibTeX

Code (0)

등록된 구현이 없습니다.

Tasks

Meta-LearningTime SeriesTime Series Analysis Time Series Forecasting

A Meta-learning based Distribution System Load Forecasting Model Selection Framework

2020-09-25 · Yiyan Li, Si Zhang, Rongxing Hu, Ning Lu

This paper presents a meta-learning based, automatic distribution system load forecasting model selection framework. The framework includes the following processes: feature extraction, candidate model labeling, offline t…

Load Forecasting Meta-Learning Model Selection

Two-Step Meta-Learning for Time-Series Forecasting Ensemble

2020-11-20 · Evaldas Vaiciukynas, Paulius Danenas, Vilius Kontrimas, Rimantas Butleris

Amounts of historical data collected increase and business intelligence applicability with automatic forecasting of time series are in high demand. While no single time series modeling method is universal to all types of…

Diversity Meta-Learning Time Series Time Series Analysis+2

Cross-Frequency Time Series Meta-Forecasting

2023-02-04 · Mike Van Ness, Huibin Shen, Hao Wang, Xiaoyong Jin 외

Meta-forecasting is a newly emerging field which combines meta-learning and time series forecasting. The goal of meta-forecasting is to train over a collection of source time series and generalize to new time series one-…

Meta-Learning Time Series Time Series Analysis Time Series Forecasting

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

2020-10-16 · Rakshitha Godahewa, Christoph Bergmeir, Geoffrey I. Webb, Pablo Montero-Manso

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 dedic…

Meta-Learning regression Time Series Time Series Analysis+1

Metadata Matters for Time Series: Informative Forecasting with Transformers

2024-10-04 · Jiaxiang Dong, Haixu Wu, Yuxuan Wang, Li Zhang 외

Time series forecasting is prevalent in extensive real-world applications, such as financial analysis and energy planning. Previous studies primarily focus on time series modality, endeavoring to capture the intricate va…

Financial Analysis Time Series Time Series Forecasting