Boosted Embeddings for Time-Series Forecasting

Sankeerth Rao Karingula, Nandini Ramanan, Rasool Tahmasbi, Mehrnaz Amjadi, Deokwoo Jung, Ricky Si, Charanraj Thimmisetty, Luisa F. Polanía, Marjorie Sayer, Jake Taylor, Claudionor Nunes Coelho

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, and Seq2Seq have been explored for the 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 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.