  Time series forecasting is a crucial task in machine learning, as it has a
wide range of applications including but not limited to forecasting electricity
consumption, traffic, and air quality. Traditional forecasting models relied on
rolling averages, vector auto-regression and auto-regressive integrated moving
averages. On the other hand, deep learning and matrix factorization models have
been recently proposed to tackle the same problem with more competitive
performance. However, one major drawback of such models is that they tend to be
overly complex in comparison to traditional techniques. In this paper, we try
to answer whether these highly complex deep learning models are without
alternative. We aim to enrich the pool of simple but powerful baselines by
revisiting the gradient boosting regression trees for time series forecasting.
Specifically, we reconfigure the way time series data is handled by Gradient
Tree Boosting models in a windowed fashion that is similar to the deep learning
models. For each training window, the target values are concatenated with
external features, and then flattened to form one input instance for a
multi-output gradient boosting regression tree model. We conducted a
comparative study on nine datasets for eight state-of-the-art deep-learning
models that were presented at top-level conferences in the last years. The
results demonstrated that the proposed approach outperforms all of the
state-of-the-art models.
