Procedia Computer Science · 2022 · 15 citations · 17 references
ComputingComputer ScienceData ScienceForecastingMachine LearningTime Series AnalysisDeep LearningTime Series Feature EngineeringTime Series Feature ExtractionTime Series ForecastingTime Series ModelingTime Series PredictionDeep Feature LearningDeep Learning For ForecastingDeep Learning For Time Series ForecastingDeep Learning-based ForecastingMachine Learning Forecasting ModelsMachine Learning-based ForecastingNon-stationary Time SeriesShipping Operations
Baltic Dry Index (BDI) is a widely used index that measures the changes in the transportation freight rates of the dry bulk shipping market, which could also serve as an indicator of the prosperity of both general shipping market and the world economy. As a typical non-stationary and non-linear time series, BDI is influenced by a variety of complicated factors, and thus accurately forecasting BDI is undoubtedly an important and challenging task. In this paper, a deep learning based framework for BDI forecasting is proposed, which contains a deep learning based feature extraction process and a machine learning regression process. Compared with traditional econometric and machine learning methods, this framework could greatly boost the prediction accuracy through capturing some in-depth features.
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Forecasting the price of Bitcoin using deep learning
Mingxi Liu, Guowen Li, Jianping Li et al. · Finance research letters · 2020 · 139 citations