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Yi-Ting Chen
Researcher at National Chiao Tung University
Publications - 9
Citations - 129
Yi-Ting Chen is an academic researcher from National Chiao Tung University. The author has contributed to research in topics: Wavelet & Wavelet transform. The author has an hindex of 5, co-authored 9 publications receiving 82 citations. Previous affiliations of Yi-Ting Chen include University of Montpellier.
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Generalized optimal wavelet decomposing algorithm for big financial data
TL;DR: A new wavelet-based methodology is proposed (named GOWDA, i.e., the generalized optimal wavelet decomposition algorithm) that allows to deconstruct price series into the true efficient price and microstructure noise, particularly for the noise that induces the phase transition behaviors.
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Pragmatic real-time logistics management with traffic IoT infrastructure: Big data predictive analytics of freight travel time for Logistics 4.0
TL;DR: A novel method to forecast travel time based on big data collected from the industrial IoT infrastructure that separates the global regression tree model based on the gradient boosting decision tree into several partitions to capture the time-varying features simultaneously.
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Jump Detection and Noise Separation by a Singular Wavelet Method for Predictive Analytics of High-Frequency Data
TL;DR: This article uses a recurrently adaptive separation algorithm, which is based on the maximal overlap discrete wavelet transform (MODWT) and that can effectively identify the time-variant jumps, extract the time -consistent patterns from the noise (jumps), and denoise the marginal perturbations.
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Improving model performance with the integrated wavelet denoising method
TL;DR: A new integrated wavelet Denoising method, named smoothness-oriented wavelet denoising algorithm (SOWDA), that optimally determines the wavelet function, maximal level of decomposition, and the threshold rule by using a smoothness score function that simultaneously detects the global and local extrema.
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Machine learning with parallel neural networks for analyzing and forecasting electricity demand
TL;DR: A novel machine learning method originating from the parallel neural networks for robust monitoring and forecasting power demand to enhance supervisory control and data acquisition for new industrial tendency such as Industry 4.0 and Energy IoT is proposed.