@conference {308,
title = {A specialized lazy learner for time series forecasting},
booktitle = {17th International Conference on Computational and Mathematical Methods in Science and Engineering, CMMSE 2017},
year = {2017},
note = {TIN2015-68854-R},
month = {7},
pages = {1397{\textendash}1403},
address = {Costa Ballena, Rota, C{\'a}adiz (Spain)},
abstract = {In a time series context the nearest neighbour algorithm looks for the historical observations most similar to the latest observations of the time series. However, some nearest neighbours can be misleading. In this paper we propose that, if prior information about the structure of the time series is known, the search space of possible neighbours can be narrowed so that some possibly misleading neighbours are avoided. This way a more effective forecasting method can be obtained.},
isbn = {978-84-617-8694-7},
author = {Francisco Mart{\'\i}nez and M.P. Fr{\'\i}as and Francisco Charte and Antonio J. Rivera}
}