The great time series classification bake off: a review and experimental evaluation of recent algorithmic advances

In the last five years there have been a large number of new time series classification algorithms proposed in the literature. These algorithms have been evaluated on subsets of the 47 data sets in the University of California, Riverside time series classification archive. The archive has recently been expanded to 85 data sets, over half of which have been donated by researchers at the University of East Anglia. Aspects of previous evaluations have made comparisons between algorithms difficult. For example, several different programming languages have been used, experiments involved a single train/test split and some used normalised data whilst others did not. The relaunch of the archive provides a timely opportunity to thoroughly evaluate algorithms on a larger number of datasets. We have implemented 18 recently proposed algorithms in a common Java framework and compared them against two standard benchmark classifiers (and each other) by performing 100 resampling experiments on each of the 85 datasets. We use these results to test several hypotheses relating to whether the algorithms are significantly more accurate than the benchmarks and each other. Our results indicate that only 9 of these algorithms are significantly more accurate than both benchmarks and that one classifier, the Collective of Transformation Ensembles, is significantly more accurate than all of the others. All of our experiments and results are reproducible: we release all of our code, results and experimental details and we hope these experiments form the basis for more rigorous testing of new algorithms in the future

Stochastic Processes and Time Series

The talk aims at discussing the main linear models for time series with short and long memory dynamics

Modelling consumer preferences

The CUB model is a mixture distribution recently proposed in literature for modeling ordinal data whose parameters may be related to explanatory variables characterizing the raters or the object of evaluation. Although various methodological aspects of this class of models have been investigated, the problem of multivariate ordinal data representation is still open. In this article the Plackett's distribution is used in order to construct a bivariate distribution from CUB margins. Furthermore, the model is extended so that the effect of raters' characteristics on their stated preferences is included

Joint modelling of ordinal data: a copula based approach

In this article we present an innovative technique to construct a multivariate distribution from margins described by CUB models. In particular, we use the Plackett distribution as a copula function, and we apply the discrete vine pair copula construction method to achieve a computational efficient solution. The proposed approach will be applied to model the importance of three key drivers of extra-virgin oil consumption in Italy

Gender differences in the perception of inflation

Using data from Italy (1994–2018), we investigate gender differences in consumers’ inflation perceptions over time. We introduce a dynamic model in order to detect the changes in the shape of the probability distributions of judgments across time and to compare the behavior of the two groups of respondents. The model components describe the deep conviction of respondents about past inflation and the uncertainty generated by the intrinsic fuzziness surrounding the evaluation process. The results suggest that women tend to perceive a higher level of inflation than men, but this propensity has changed over the years. The Euro changeover and other economic events produced an increase in the heterogeneity of men’s responses and decreased the gap between the feelings of men and women about inflation. When the perceived inflation closely tracked the true rate, the gender difference was more pronounced because of the smaller heterogeneity and the higher asymmetry in the distribution of women’s judgments