In this paper we describe and apply the methods of Symbolic Time Series Analysis to an experimental framework. We discuss data symbolization as a tool for identifying temporal patterns in experimental data and use symbol sequence statistics in a model strategy. In particular, we introduce a...

In this paper I describe and apply the methods of Symbolic Time Series Analysis (STSA) to an experimental framework. The idea behind Symbolic Time Series Analysis is simple: the values of a given time series data are transformed into a finite set of symbols obtaining a finite string. Then, we...

Infra-monthly time series have increasingly appeared on the radar of official statistics in recent years, mostly as a consequence of a general digital transformation process and the outbreak of the COVID-19 pandemic in 2020. Many of those series are seasonal and thus in need for seasonal...

Infra-monthly time series have increasingly appeared on the radar of official statistics in recent years, mostly as a consequence of a general digital transformation process and the outbreak of the COVID-19 pandemic in 2020. Many of those series are seasonal and thus in need for seasonal...

Infra-monthly economic time series have become increasingly popular in official statistics in recent years. This evolution has been largely fostered by official statistics’ digital transformation during the last decade. The COVID-19 pandemic outbreak in 2020 has added fuel to the fire as many...

We propose to date and analyze the financial cycle using the Maximum Overlap Discrete Wavelet Transform (MODWT). Our presentation points out limitations of the methods derived from the classical business cycle literature, while stressing their connection with wavelet analysis. The fundamental...

The dynamic comovement between time series is a key concept in macroeconomic analysis. The extent to which series are cyclically synchronized is particularly important for evaluating the feasibility of common policy measures for groups of countries. This paper investigates concepts in the time...

Forecast combination has been proven to be a very important technique to obtain accurate predictions for various applications in economics, finance, marketing and many other areas. In many applications, forecast errors exhibit heavy-tailed behaviors for various reasons. Unfortunately, to our...

The purpose of this work is to study the statistical properties of the MDD for stochastic processes characterized by the stylized facts of real financial time series. The numerical results obtained using a Monte Carlo code are firstly validated against the analytical predictions available within...

Statistical learning models have profoundly changed the rules of trading on the stock exchange. Quantitative analysts try to utilise them predict potential profits and risks in a better manner. However, the available studies are mostly focused on testing the increasingly complex machine learning...