Maria Eduarda Silva

Abstract
In recent years, there has been a surge in the prevalence of high- and multidimensional temporal data across various scientific disciplines. These datasets are characterized by their vast size and challenging potential for analysis. Such data typically exhibit serial and cross-dependency and possess high dimensionality, thereby introducing additional complexities to conventional time series analysis methods. To address these challenges, a recent and complementary approach has emerged, known as network-based analysis methods for multivariate time series. In univariate settings, quantile graphs have been employed to capture temporal transition properties and reduce data dimensionality by mapping observations to a smaller set of sample quantiles. To confront the increasingly prominent issue of high dimensionality, we propose an extension of quantile graphs into a multivariate variant, which we term Multilayer Quantile Graphs. In this innovative mapping, each time series is transformed into a quantile graph, and inter-layer connections are established to link contemporaneous quantiles of pairwise series. This enables the analysis of dynamic transitions across multiple dimensions. In this study, we demonstrate the effectiveness of this new mapping using synthetic and benchmark multivariate time series datasets. We delve into the resulting network's topological structures, extract network features, and employ these features for original dataset analysis. Furthermore, we compare our results with a recent method from the literature. The resulting multilayer network offers a significant reduction in the dimensionality of the original data while capturing serial and cross-dimensional transitions. This approach facilitates the characterization and analysis of large multivariate time series datasets through network analysis techniques.

Abstract
Predictors of macroeconomic indicators rely primarily on traditional data sourced from National Statistical Offices. However, new data sources made available from recent technological advancements, namely data from online activities, have the potential to bring about fresh perspectives on monitoring economic activities and enhance the accuracy of forecasting. This paper reviews the literature on predicting macroeconomic indicators, such as the gross domestic product, unemployment rate, consumer price index or private consumption, based on online activity data sourced from Google Trends, Twitter (rebranded to X) and mobile devices. Based on a systematic search of publications indexed on the Web of Science and Scopus databases, the analysis of a final set of 56 publications covers the publication history of the data sources, the methods used to model the data and the predictive accuracy of information from such data sources. The paper also discusses the limitations and challenges of using online activity data for macroeconomic predictions. The review concludes that online activity data can be a valuable source of information for predicting macroeconomic indicators. However, one must consider certain limitations and challenges to improve the models' accuracy and reliability. © 2024 - IOS Press. All rights reserved.

Abstract
Policymakers often have to make decisions based on incomplete economic data because of the usual delay in publishing official statistics. To circumvent this issue, researchers use data from Google Trends (GT) as an early indicator of economic performance. Such data have emerged in the literature as alternative and complementary predictors of macroeconomic outcomes, such as the unemployment rate, featuring readiness, public availability and no costs. This study deals with extensive daily GT data to develop a framework to nowcast monthly unemployment rates tailored to work with real-time data availability, resorting to Mixed Data Sampling (MIDAS) regressions. Portugal is chosen as a use case for the methodology since extracting GT data requires the selection of culturally dependent keywords. The nowcasting period spans 2019 to 2021, encompassing the time frame in which the coronavirus pandemic initiated. The findings indicate that using daily GT data with MIDAS provides timely and accurate insights into the unemployment rate, especially during the COVID-19 pandemic, showing accuracy gains even when compared to nowcasts obtained from typical monthly GT data via traditional ARMAX models.

Abstract
Abstract. Palaeoclimate time series, reflecting the state of Earth's climate in the distant past, display occasionally very large and rapid shifts, evidencing abrupt climate variability. The identification and characterisation of these abrupt transitions in palaeoclimate records is of particular interest as it allows the understanding of millennial climate variability and the identification of potential tipping points in the context of current climate change. Methods that are able to characterise these events in an objective and automatic way, in a single time series or across two proxy records, are therefore of particular interest. In our study the matrix profile approach is used to describe Dansgaard-Oeschger (DO) events, abrupt warmings detected in Greenland ice core, and Northern Hemisphere marine and continental records. The results indicate that canonical events DO-19 and DO-20, occurring at around 72 and 76 ka, are the most similar events over the past 110,000 years. These transitions are characterised by matching transitions corresponding to events DO-1, DO-8 and DO-12. These transitions are abrupt, resulting in a rapid shift to warmer conditions, followed by a gradual return to cold conditions. The joint analysis of the d18O and Ca2+ time series indicates that the transition corresponding to the DO-19 event is the most similar event across the two time series.

Abstract
Censored data are frequently found in diverse fields including environmental monitoring, medicine, economics and social sciences. Censoring occurs when observations are available only for a restricted range, e.g., due to a detection limit. Ignoring censoring produces biased estimates and unreliable statistical inference. The aim of this work is to contribute to the modelling of time series of counts under censoring using convolution closed infinitely divisible (CCID) models. The emphasis is on estimation and inference problems, using Bayesian approaches with Approximate Bayesian Computation (ABC) and Gibbs sampler with Data Augmentation (GDA) algorithms.