Two-Step Meta-Learning for Time-Series Forecasting Ensemble
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 dynamics, forecasting using an ensemble of several methods is often seen as a compromise. Instead of fixing ensemble diversity and size, we propose to predict these aspects adaptively using meta-learning. Meta-learning here considers two separate random forest regression models, built on 390 time-series features, to rank 22 univariate forecasting methods and recommend ensemble size. The forecasting ensemble is consequently formed from methods ranked as the best, and forecasts are pooled using either simple or weighted average (with a weight corresponding to reciprocal rank). The proposed approach was tested on 12561 micro-economic time-series (expanded to 38633 for various forecasting horizons) of M4 competition where meta-learning outperformed Theta and Comb benchmarks by relative forecasting errors for all data types and horizons. Best overall results were achieved by weighted pooling with a symmetric mean absolute percentage error of 9.21% versus 11.05% obtained using the Theta method.
Code (0)
등록된 구현이 없습니다.
Tasks
DiversityMeta-LearningTime SeriesTime Series AnalysisTime Series ForecastingVocal Bursts Valence PredictionSimilar Papers 제목 키워드 기반
A study on Ensemble Learning for Time Series Forecasting and the need for Meta-Learning
The contribution of this work is twofold: (1) We introduce a collection of ensemble methods for time series forecasting to combine predictions from base models. We demonstrate insights on the power of ensemble learning f…
Ensemble LearningMeta-LearningTime SeriesTime Series Analysis+1Attention-Based Ensemble Pooling for Time Series Forecasting
A common technique to reduce model bias in time-series forecasting is to use an ensemble of predictive models and pool their output into an ensemble forecast. In cases where each predictive model has different biases, ho…
Time SeriesTime Series ForecastingvalidCross-Frequency Time Series Meta-Forecasting
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-LearningTime SeriesTime Series AnalysisTime Series ForecastingAutomatic Forecasting via Meta-Learning
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 …
Meta-LearningTime SeriesTime Series AnalysisTime Series ForecastingMultistep Brent Oil Price Forecasting with a Multi-Aspect Meta-heuristic Optimization and Ensemble Deep Learning Model
Accurate crude oil price forecasting is crucial for various economic activities, including energy trading, risk management, and investment planning. Although deep learning models have emerged as powerful tools for crude …
energy tradingfeature selectionManagementMetaheuristic Optimization+1