  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.
