| TSclust: An R package for time series clustering P Montero, JA Vilar Journal of Statistical Software 62, 1-43, 2015 | 631 | 2015 |
| FFORMA: Feature-based forecast model averaging P Montero-Manso, G Athanasopoulos, RJ Hyndman, TS Talagala International Journal of Forecasting 36 (1), 86-92, 2020 | 334 | 2020 |
| Monash time series forecasting archive R Godahewa, C Bergmeir, GI Webb, RJ Hyndman, P Montero-Manso Thirty-fifth Conference on Neural Information Processing Systems Datasets …, 2021 | 193 | 2021 |
| Principles and algorithms for forecasting groups of time series: Locality and globality P Montero-Manso, RJ Hyndman International Journal of Forecasting 37 (4), 1632-1653, 2021 | 190 | 2021 |
| tsfeatures: Time series feature extraction R Hyndman, Y Kang, P Montero-Manso, T Talagala, E Wang, Y Yang, ... R package version 1 (0), 2019 | 87 | 2019 |
| Predictive performance of multi-model ensemble forecasts of COVID-19 across European nations K Sherratt, H Gruson, R Grah, H Johnson, R Niehus, B Prasse, ... Elife 12, e81916, 2023 | 69 | 2023 |
| tsfeatures: Time series feature extraction. R package version 1.0. 2 R Hyndman, Y Kang, P Montero-Manso, T Talagala, E Wang, Y Yang, ... | 41 | 2020 |
| M4comp2018: Data from the M4-Competition P Montero-Manso, C Netto, T Talagala R package version 0.1. 0, 2018 | 18 | 2018 |
| tsfeatures: time series feature extraction, 2020 RJ Hyndman, Y Kang, P Montero-Manso, T Talagala, E Wang, Y Yang, ... R package version 1 (2.9000), 2020 | 17 | 2020 |
| An accurate and fully-automated ensemble model for weekly time series forecasting R Godahewa, C Bergmeir, GI Webb, P Montero-Manso International Journal of Forecasting 39 (2), 641-658, 2023 | 11 | 2023 |
| A strong baseline for weekly time series forecasting R Godahewa, C Bergmeir, GI Webb, P Montero-Manso arXiv preprint arXiv:2010.08158, 2020 | 9 | 2020 |
| TSclust: Time series clustering utilities P Montero, JA Vilar R package version 1 (1), 2014 | 9 | 2014 |
| Comparison and Evaluation of Methods for a Predict+ Optimize Problem in Renewable Energy C Bergmeir, F de Nijs, A Sriramulu, M Abolghasemi, R Bean, J Betts, ... arXiv preprint arXiv:2212.10723, 2022 | 8 | 2022 |
| A first perturbome of Pseudomonas aeruginosa: Identification of core genes related to multiple perturbations by a machine learning approach JAM Mora, P Montero-Manso, R García-Batán, R Campos-Sánchez, ... Biosystems 205, 104411, 2021 | 8 | 2021 |
| A Look at the Evaluation Setup of the M5 Forecasting Competition H Hewamalage, P Montero-Manso, C Bergmeir, RJ Hyndman arXiv preprint arXiv:2108.03588, 2021 | 7 | 2021 |
| Two‐sample homogeneity testing: A procedure based on comparing distributions of interpoint distances P Montero‐Manso, JA Vilar Statistical Analysis and Data Mining: The ASA Data Science Journal 12 (3 …, 2019 | 6 | 2019 |
| M., A Package for Stationary Time Series Clustering P Manso Master thesis, Universidade da Coruna, 2013 | 6 | 2013 |
| Distributed classification based on distances between probability distributions in feature space P Montero-Manso, L Morán-Fernández, V Bolón-Canedo, JA Vilar, ... Information Sciences 496, 431-450, 2019 | 5 | 2019 |
| Situational assessment of COVID-19 in Australia Technical Report 15 March 2021 (released 28 May 2021) N Golding, FM Shearer, R Moss, P Dawson, D Liu, JV Ross, R Hyndman, ... | 5* | |
| How to Leverage Data for Time Series Forecasting with Artificial Intelligence Models: Illustrations and Guidelines for Cross-Learning P Montero-Manso Forecasting with Artificial Intelligence: Theory and Applications, 123-162, 2023 | 4 | 2023 |
