Search results for " time series"
showing 10 items of 75 documents
Forecasting : theory and practice
2022
Forecasting has always been at the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The large number of forecasting applications calls for a diverse set of forecasting methods to tackle real-life challenges. This article provides a non-systematic review of the theory and the practice of forecasting. We provide an overview of a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a varie…
Multiscale Information Decomposition: Exact Computation for Multivariate Gaussian Processes
2017
Exploiting the theory of state space models, we derive the exact expressions of the information transfer, as well as redundant and synergistic transfer, for coupled Gaussian processes observed at multiple temporal scales. All of the terms, constituting the frameworks known as interaction information decomposition and partial information decomposition, can thus be analytically obtained for different time scales from the parameters of the VAR model that fits the processes. We report the application of the proposed methodology firstly to benchmark Gaussian systems, showing that this class of systems may generate patterns of information decomposition characterized by prevalently redundant or sy…
Mixture Hidden Markov Models for Sequence Data: The seqHMM Package in R
2019
Sequence analysis is being more and more widely used for the analysis of social sequences and other multivariate categorical time series data. However, it is often complex to describe, visualize, and compare large sequence data, especially when there are multiple parallel sequences per subject. Hidden (latent) Markov models (HMMs) are able to detect underlying latent structures and they can be used in various longitudinal settings: to account for measurement error, to detect unobservable states, or to compress information across several types of observations. Extending to mixture hidden Markov models (MHMMs) allows clustering data into homogeneous subsets, with or without external covariate…
KFAS : Exponential Family State Space Models in R
2017
State space modelling is an efficient and flexible method for statistical inference of a broad class of time series and other data. This paper describes an R package KFAS for state space modelling with the observations from an exponential family, namely Gaussian, Poisson, binomial, negative binomial and gamma distributions. After introducing the basic theory behind Gaussian and non-Gaussian state space models, an illustrative example of Poisson time series forecasting is provided. Finally, a comparison to alternative R packages suitable for non-Gaussian time series modelling is presented.
Multiscale partial information decomposition of dynamic processes with short and long-range correlations: theory and application to cardiovascular co…
2022
Abstract Objective. In this work, an analytical framework for the multiscale analysis of multivariate Gaussian processes is presented, whereby the computation of Partial Information Decomposition measures is achieved accounting for the simultaneous presence of short-term dynamics and long-range correlations. Approach. We consider physiological time series mapping the activity of the cardiac, vascular and respiratory systems in the field of Network Physiology. In this context, the multiscale representation of transfer entropy within the network of interactions among Systolic arterial pressure (S), respiration (R) and heart period (H), as well as the decomposition into unique, redundant and s…
Using the Scaling Analysis to Characterize Financial Markets
2003
We empirically analyze the scaling properties of daily Foreign Exchange rates, Stock Market indices and Bond futures across different financial markets. We study the scaling behaviour of the time series by using a generalized Hurst exponent approach. We verify the robustness of this approach and we compare the results with the scaling properties in the frequency-domain. We find evidence of deviations from the pure Brownian motion behavior. We show that these deviations are associated with characteristics of the specific markets and they can be, therefore, used to distinguish the different degrees of development of the markets.
Investigating effects in GNSS station coordinate time series
2014
The vertical and horizontal displacements of the Earth can be measured to a high degree of precision using GNSS. Time series of Latvian GNSS station positions of both the EUPOS®-Riga and LatPos networks have been developed at the Institute of Geodesy and Geoinformation of the University of Latvia (LU GGI). In this study the main focus is made on the noise analysis of the obtained time series and site displacement identification. The results of time series have been analysed and distinctive behaviour of EUPOS®-Riga and LatPos station coordinate changes have been identified. The possible dependences of GNSS station coordinate distribution on EPN station problems, seismic activity of some area…
Testing for Government Intertemporal Solvency: A Smooth Transition Error Correction Model Approach
2001
Applied macroeconomists have tested for the government intertemporal solvency condition by either testing for linear stationarity in the total government deficit series or testing for linear cointegration between total government spending and total tax revenues. A number of authors have focused, in particular, on structural breaks in the government deficit process. In this paper, we use a smooth transition error correction model to test and estimate a shift in the adjustment toward a linear cointegration relationship between the government spending to output ratio and the total tax revenues to output ratio. Estimation results show that government authorities react only to large (in absolute…
¿SE PUEDE MEDIR LA NEGOCIACIÓN INFORMADA?: UNA REVISIÓN DE LA METODOLOGÍA BASADA EN LAS COVARIANZAS DE LAS SERIES DE PRECIOS / CAN WE MEASURE THE INS…
2009
El desarrollo en los modelos teóricos de microestructura ha motivado la aparición de un grupo de trabajos encaminado al estudio empírico de los costes de transacción y sus componentes dada la importancia que han tenido los mismos en el estudio del funcionamiento de los mercados y la comparación entre éstos así como sus numerosas aplicaciones en campos afines (finanzas corporativas, eficiencia de los mercados, etc.). Por otra parte, la contrastación empírica de los distintos modelos establecidos muestra resultados claramente dispares. Por ello, el objetivo de nuestro trabajo es analizar con detalle y en conjunto dichos modelos centrándonos en un grupo con características muy similares. Concr…
A complex network analysis of inbound tourism in Sicily
2019
In this article, the complex dynamics of inbound tourism in Sicily is analyzed for the period 1998–2017. The horizontal visibility graph algorithm is used to transform the overnight stays' time series into a network whose topology is investigated by standard network analysis. Discontinuities in the domestic and international tourism demand were identified in order to detect signals of change and the timing of the directional change in tourism growth. The network degree distribution confirms the complex structure of the destination and reveals the random and thus more unpredictable nature of the international tourism demand in Sicily, compared with a more stable domestic segment. Some policy…