Evaluating time series forecasting models: An empirical study on performance estimation methods
This paper describes an empirical study and comparison of different performance estimation methods in the context of time series forecasting.
published in Machine Learning journal Abstract. Performance estimation aims at estimating the loss that a predictive model will incur on unseen data.
Arbitrage of Forecasting Experts
This paper describes our award-winning (Best Student Machine Learning Paper Award given by the Machine Learning Journal at the European Conference on Machine Learnig (ECML/PKDD’2017)) work on ensembles for time series forecasting.
How to evaluate sentiment classifiers for Twitter time-ordered data?
Social media are becoming an increasingly important source of information about the public mood regarding issues such as elections, Brexit, stock market, etc. In this paper we focus on how to evaluate sentiment classifiers for Twitter time-ordered data.
Data Mining with R: Learning with Case Studies, Second Edition
This is the second edition of my widely acclaimed Data Mining with R book initially published by CRC Press in 2010, with this strongly revised second edition appearing in 2017.
A Survey of Predictive Modeling on Imbalanced Domains
This paper presents an extensive survey of predictive analytics methods for handling problems where the distribution of the target variable (norminal/classification or numeric/regression) is highly imbalanced, and moreover the less frequent values are the more relevant for the end-user.