2022 | OriginalPaper | Buchkapitel
Boosted Embeddings for Time-Series Forecasting
verfasst von : Sankeerth Rao Karingula, Nandini Ramanan, Rasool Tahmasbi, Mehrnaz Amjadi, Deokwoo Jung, Ricky Si, Charanraj Thimmisetty, Luisa F. Polania, Marjorie Sayer, Jake Taylor, Claudionor Nunes Coelho Jr
Erschienen in: Machine Learning, Optimization, and Data Science
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Abstract
DeepGB
. We formulate and implement a variant of gradient boosting wherein the weak learners are deep neural networks whose weights are incrementally found in a greedy manner over iterations. In particular, we develop a new embedding architecture that improves the performance of many deep learning models on time-series data using a gradient boosting variant. We demonstrate that our model outperforms existing comparable state-of-the-art methods using real-world sensor data and public data sets.