Abstract

Abstract
Performance estimation aims at estimating the loss that a predictive model will incur on unseen data. This process is a fundamental stage in any machine learning project. In this paper we study the application of these methods to time series forecasting tasks. For independent and identically distributed data the most common approach is cross-validation. However, the dependency among observations in time series raises some caveats about the most appropriate way to estimate performance in this type of data. Currently, there is no consensual approach. We contribute to the literature by presenting an extensive empirical study which compares different performance estimation methods for time series forecasting tasks. These methods include variants of cross-validation, out-of-sample (holdout), and prequential approaches. Two case studies are analysed: One with 174 real-world time series and another with three synthetic time series. Results show noticeable differences in the performance estimation methods in the two scenarios. In particular, empirical experiments suggest that blocked cross-validation can be applied to stationary time series. However, when the time series are non-stationary, the most accurate estimates are produced by out-of-sample methods, particularly the holdout approach repeated in multiple testing periods.

Abstract
Time series forecasting is one of the most active research topics. Machine learning methods have been increasingly adopted to solve these predictive tasks. However, in a recent work, evidence was shown that these approaches systematically present a lower predictive performance relative to simple statistical methods. In this work, we counter these results. We show that these are only valid under an extremely low sample size. Using a learning curve method, our results suggest that machine learning methods improve their relative predictive performance as the sample size grows. The R code to reproduce all of our experiments is available at https://github.com/vcerqueira/MLforForecasting.

Abstract
Several sophisticated machine learning tools (e.g., ensembles or deep networks) have shown outstanding performance in different regression forecasting tasks. In many real world application domains the numeric predictions of the models drive important and costly decisions. Nevertheless, decision makers frequently require more than a black box model to be able to "trust" the predictions up to the point that they base their decisions on them. In this context, understanding these black boxes has become one of the hot topics in Machine Learning research. This paper proposes a series of visualization tools that explain the relationship between the expected predictive performance of black box regression models and the values of the input variables of any given test case. This type of information thus allows end-users to correctly assess the risks associated with the use of a model, by showing how concrete values of the predictors may affect the performance of the model. Our illustrations with different real world data sets and learning algorithms provide insights on the type of usage and information these tools bring to both the data analyst and the end-user. Furthermore, a thorough evaluation of the proposed tools is performed to showcase the reliability of this approach.

Abstract
Machine learning algorithms have shown several advantages compared to humans, namely in terms of the scale of data that can be analysed, delivering high speed and precision. However, it is not always possible to understand how algorithms work. As a result of the complexity of some algorithms, users started to feel the need to ask for explanations, boosting the relevance of Explainable Artificial Intelligence. This field aims to explain and interpret models with the use of specific analytical methods that usually analyse how their predicted values and/or errors behave. While prediction analysis is widely studied, performance analysis has limitations for regression models. This paper proposes a rule-based approach, Error Distribution Rules (EDRs), to uncover atypical error regions, while considering multivariate feature interactions without size restrictions. Extracting EDRs is a form of subgroup mining. EDRs are model agnostic and a drill-down technique to evaluate regression models, which consider multivariate interactions between predictors. EDRs uncover regions of the input space with deviating performance providing an interpretable description of these regions. They can be regarded as a complementary tool to the standard reporting of the expected average predictive performance. Moreover, by providing interpretable descriptions of these specific regions, EDRs allow end users to understand the dangers of using regression tools for some specific cases that fall on these regions, that is, they improve the accountability of models. The performance of several models from different problems was studied, showing that our proposal allows the analysis of many situations and direct model comparison. In order to facilitate the examination of rules, two visualization tools based on boxplots and density plots were implemented. A network visualization tool is also provided to rapidly check interactions of every feature condition. An additional tool is provided by using a grid of boxplots, where comparison between quartiles of every distribution with a reference is performed. Based on this comparison, an extrapolation of counterfactual examples to regression was also implemented. A set of examples is described, including a setting where regression models performance is compared in detail using EDRs. Specifically, the error difference between two models in a dataset is studied by deriving rules highlighting regions of the input space where model performance difference is unexpected. The application of visual tools is illustrated using EDRs examples derived from public available datasets. Also, case studies illustrating the specialization of subgroups, identification of counter factual subgroups and detecting unanticipated complex models are presented. This paper extends the state of the art by providing a method to derive explanations for model performance instead of explanations for model predictions.

Abstract
Fraud detection is a key activity with serious socioeconomical impact. Inspection activities associated with this task are usually constrained by limited available resources. Data analysis methods can provide help in the task of deciding where to allocate these limited resources in order to optimise the outcome of the inspection activities. This paper presents a multi-strategy learning method to address the question of which cases to inspect first. The proposed methodology is based on the utility theory and provides a ranking ordered by decreasing expected outcome of inspecting the candidate cases. This outcome is a function not only of the probability of the case being fraudulent but also of the inspection costs and expected payoff if the case is confirmed as a fraud. The proposed methodology is general and can be useful on fraud detection activities with limited inspection resources. We experimentally evaluate our proposal on both an artificial domain and on a real world task.