Modeling Time Series Data with Deep Learning: A Review, Analysis, Evaluation and Future Trend
John-Syin Ang, Kok-Why Ng, Fang-Fang Chua
Abstract
Time series modeling is a challenging and demanding problem. In the recent year, deep learning (DL) has attracted huge attention in many fields of research, including time series analysis and forecasting. While the methods of DL are very broad and wide, we aim to review the most recent and impactful deep learning papers in order to provide insights from the notable DL models and evaluation methods on time series problems. Our main objective is to review and analyse the advantages and disadvantages of different models, evaluation methods, future trends and techniques of solving time series problem with DL.
Topics & Concepts
Computer scienceTime seriesSeries (stratigraphy)Deep learningArtificial intelligenceMachine learningData scienceBiologyPaleontologyTime Series Analysis and ForecastingAnomaly Detection Techniques and ApplicationsStock Market Forecasting Methods