Search Machine Learning Repository: @inproceedings{icml2014c1_taieb14,
    Publisher = {JMLR Workshop and Conference Proceedings},
    Title = {Boosting multi-step autoregressive forecasts},
    Url = {http://jmlr.org/proceedings/papers/v32/taieb14.pdf},
    Abstract = {Multi-step forecasts can be produced recursively by iterating a one-step model, or directly using a specific model for each horizon. Choosing between these two strategies is not an easy task since it involves a trade-off between bias and estimation variance over the forecast horizon. Using a nonlinear machine learning model makes the tradeoff even more difficult. To address this issue, we propose a new forecasting strategy which boosts traditional recursive linear forecasts with a direct strategy using a boosting autoregression procedure at each horizon. First, we investigate the performance of the proposed strategy in terms of bias and variance decomposition of the error using simulated time series. Then, we evaluate the proposed strategy on real-world time series from two forecasting competitions. Overall, we obtain excellent performance with respect to the standard forecasting strategies.},
    Author = {Souhaib B. Taieb and Rob Hyndman},
    Editor = {Tony Jebara and Eric P. Xing},
    Year = {2014},
    Booktitle = {Proceedings of the 31st International Conference on Machine Learning (ICML-14)},
    Pages = {109-117}
   }