Boosted Embeddings for Time Series Forecasting

@inproceedings{Karingula2021BoostedEF,
  title={Boosted Embeddings for Time Series Forecasting},
  author={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},
  booktitle={International Conference on Machine Learning, Optimization, and Data Science},
  year={2021}
}
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… 
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6 Citations

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