A deep learning-based method is proposed for time series forecasting that incorporates feature selection to improve the efficacy and interpretability of the model. The proposed method includes an additional layer at the top of the model which selects the relevant features and time steps.
Oct 1, 2023
This paper presents a deep learning-based method for time series forecasting that incorporates feature selection to improve model efficacy and interpretability.
Oct 1, 2023 · This paper presents a deep learning-based method for time series forecasting that incorporates feature selection to improve model efficacy and interpretability.
Embedded Temporal Feature Selection for Time Series Forecasting Using Deep Learning · List of references · Publications that cite this publication.
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Apr 10, 2023 · DL models are useful with those as they have feature engineering embedded within and can capture complex patterns that classical algos cannot.
Dec 29, 2023 · In this article, we present a novel feature selection method embedded in Long Short-Term Memory networks, leveraging a multi-objective evolutionary algorithm.
Feb 20, 2024 · This paper presents a deep learning-based method for time series forecasting that incorporates feature selection to improve model efficacy and ...
Introduction of an embedded Feature Selection (FS) method tailored for time-series forecasting, significantly enhancing Deep Learning model performance.
List of research papers focus on time series forecasting and deep learning, as well as other resources like competitions, datasets, courses, blogs, code, etc.
Nov 6, 2023 · Feature selection for time series forecasting often requires additional steps like detrending, deseasonalizing, and ensuring that all features are relevant.