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Symbolic Time Series Analysis and Economic Regimes

Brida, Juan Gabriel - 2011

theory and symbolic dynamics. I discuss data symbolization as a tool for identifying temporal patterns in experimental data …

Persistent link: https://www.econbiz.de/10014138108

Methodological Aspects of Time Series Back-Calculation

Caporin, Massimiliano; Sartore, Domenico - 2012

This paper provides the theoretical and operational framework for estimating past values of relevant time series starting from a (limited) information set. We consider a general approach that includes as special cases time series aggregation and temporal and/or spatial disaggregation problems....

Persistent link: https://www.econbiz.de/10014053993

Exploring Two Inflationary Regimes in Latin-American Economies : A Binary Time Series Analysis

Brida, Juan Gabriel; Garrido, Nicolas - 2011

The aim of this paper is to apply the methods of Symbolic Time Series Analysis (STSA) to series of inflation from a group of Latin-American economies. Starting with a partition of two inflation regimes, we use data symbolization for identifying temporal patterns. Afterwards the statistical...

Persistent link: https://www.econbiz.de/10014062841

A Note on the Recursions of Multichannel Complex Subset Autoregressions - Financial and Economic Forecasting (Chapter 2)

Penm, Jack H.W; Penm, Jammie H.; Terrell, R. Deane - 2002

The recursive algorithm to select the optimum multivariate real subset autoregressive model (AR) [1] is generalized to apply to multichannel complex subset AR's. It is initiated by fitting all 'forward' and 'backward' one-lag AR's. The method then allows one to develop successively all complex...

Persistent link: https://www.econbiz.de/10014101443

On the Recursive Fitting of Subset Autoregressions - Financial and Economic Forecasting (Chapter 1)

Penm, Jack H.W; Penm, Jammie H.; Terrell, R. Deane - 2003

The paper comprises the preface and chapter 1 of the book titled "Financial and Economic Forecasting" (Authors: Penm-Penm-Terrell; Publication date: October 2002). The preface provides explanatory remarks at the beginning of the book. It briefly introduces theoretical developments and empirical...

Persistent link: https://www.econbiz.de/10014101531

The Shape of Performance Curve in Financial Time Series

Alonso, Miquel Noguer i; Srivastava, Sonam - 2021

in some specific domains.We discuss some of the recent discoveries in the mathematical theory of machine learning that … reduce the gap between theory and practice. We conduct experiments in the financial time series domain using deep neural … financial time series domain. This is consistent with the finance practitioner's theory that backtesting ( training data …

Persistent link: https://www.econbiz.de/10013310497

Assessing Persistence in Discrete Nonstationary Time-Series Models

McCabe, Brendan P.M; Martin, G. M.; Tremayne, Andy - 2005

The aim of this paper is to examine the application of measures of persistence in a range of time-series models nested in the framework of Cramer (1961). This framework is a generalization of the Wold (1938) decomposition for stationary time-series which, in addition to accommodating the...

Persistent link: https://www.econbiz.de/10014066817

A Spatial Invariance Principle for Non-stationary Random Fields

Tseng, Michael - 2023

We obtain an invariance principle for the two-dimensional Brownian sheet where the underlying random field need not be independent or stationary. We also provide a basic demonstration of its application towards spatial unit root testing

Persistent link: https://www.econbiz.de/10014347650

Multivariate Score-Driven Models for Count Time Series To Assess Financial Contagion

Agosto, Arianna - 2022

This paper develops a multivariate model for count time series, in which the time-varying intensity parameter determining the probability that an event occurs evolves according to general autoregressive score (GAS) models (see Creal et al., 2013; Harvey, 2013).The model is particularly suitable to...

Persistent link: https://www.econbiz.de/10013406167

Generative Models for Time Series in Finance

Noguer i Alonso, Miquel; Pacheco Aznar, David - 2023

A generative model is a statistical model of the joint probability distribution. We built a generative model for univariate time series in finance using a Variational Autoencoder (VAE) neural network architecture. We test the model in SP500 and the Heston Model widely used for option pricing and...

Persistent link: https://www.econbiz.de/10014255820