FrancisArgnR/Time-series---deep-learning---state-of-the-art
Scientific time series and deep learning state of the art
This document helps researchers and practitioners quickly find relevant academic papers on applying deep learning to time series data. It categorizes and organizes bibliographical references by deep learning model type (e.g., LSTM, Auto-Encoders) and publication year. Each reference includes a summary and additional notes, making it easier to grasp key concepts and findings. This is for anyone researching or implementing deep learning solutions for time-series related problems in fields like energy forecasting or traffic prediction.
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Use this if you need a curated bibliography of scientific papers on deep learning for time series, organized by specific deep learning architectures and publication dates.
Not ideal if you are looking for code implementations, tutorials, or a general overview of deep learning concepts rather than specific research references.
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