Boosted Embeddings for Time Series Forecasting

• URL: http://arxiv.org/abs/2104.04781v1
• Date: Sat, 10 Apr 2021 14:38:11 GMT
• Title: Boosted Embeddings for Time Series Forecasting
• Authors: Sankeerth Rao Karingula and Nandini Ramanan and Rasool Tahsambi and Mehrnaz Amjadi and Deokwoo Jung and Ricky Si and Charanraj Thimmisetty and Claudionor Nunes Coelho Jr
• Abstract summary: We propose a novel time series forecast model, DeepGB. We formulate and implement a variant of Gradient boosting wherein the weak learners are DNNs whose weights are incrementally found in a greedy manner over iterations. We demonstrate that our model outperforms existing comparable state-of-the-art models using real-world sensor data and public dataset.
• Score: 0.6042845803090501
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Time series forecasting is a fundamental task emerging from diverse data-driven applications. Many advanced autoregressive methods such as ARIMA were used to develop forecasting models. Recently, deep learning based methods such as DeepAr, NeuralProphet, Seq2Seq have been explored for time series forecasting problem. In this paper, we propose a novel time series forecast model, DeepGB. We formulate and implement a variant of Gradient boosting wherein the weak learners are DNNs 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 using Gradient boosting variant. We demonstrate that our model outperforms existing comparable state-of-the-art models using real-world sensor data and public dataset.

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