Boosted Embeddings for Time Series Forecasting

Time series forecasting is a fundamental task emerging from diverse data-driven applications. Many advanced autoregressive methods such as ARIMA[8] were used to develop forecasting models. Recently, deep learning based methods such as DeepAr[16], NeuralProphet[1], Seq2Seq [30] 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 [18] 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 [18] variant. We demonstrate that our model outperforms existing comparable state-of-the-art models using real-world sensor data and public dataset.