Search results for "aikasarjat"

Wood-decaying fungi in old-growth boreal forest fragments: extinctions and colonizations over 20 years

2021

According to ecology theory, isolated habitat fragments cannot maintain populations of specialized species. Yet, empirical evidence based on monitoring of the same fragments over time is still limited. We studied the colonization–extinction dynamics of eight wood-decaying fungal species in 16 old-growth forest fragments (<14 ha) over a 20-year period (1997–2017). We observed 19 extinctions and 5 colonizations; yet, the distribution of extinctions and colonizations did not differ from the one expected by chance for any of the species. Twenty-six percent of the extinctions took place in two natural fragments amid large forest–peatland complexes. (Romell) Bourdot and Galzin decreased …

Integrated capital shares

2019

In empirical macroeconomics, inter-dependencies between countries are often analysed using cross-country correlations or graphical investigation of time series. This study shows that applying an alternative methodological approach - identification of common unobservable factors and using them as explanatory variables for country-specific time series - indicates a stronger cross-country integration of functional income distributions than the standard methods. The results vary only little between different samples, where both the country and year coverage change. Moreover, the main findings are not sensitive to the way capital depreciation is taken into account. The primary driving factor see…

Blind recovery of sources for multivariate space-time random fields

2022

AbstractWith advances in modern worlds technology, huge datasets that show dependencies in space as well as in time occur frequently in practice. As an example, several monitoring stations at different geographical locations track hourly concentration measurements of a number of air pollutants for several years. Such a dataset contains thousands of multivariate observations, thus, proper statistical analysis needs to account for dependencies in space and time between and among the different monitored variables. To simplify the consequent multivariate spatio-temporal statistical analysis it might be of interest to detect linear transformations of the original observations that result in stra…

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.

Efficient Bayesian generalized linear models with time-varying coefficients : The walker package in R

2020

The R package walker extends standard Bayesian general linear models to the case where the effects of the explanatory variables can vary in time. This allows, for example, to model the effects of interventions such as changes in tax policy which gradually increases their effect over time. The Markov chain Monte Carlo algorithms powering the Bayesian inference are based on Hamiltonian Monte Carlo provided by Stan software, using a state space representation of the model to marginalise over the regression coefficients for efficient low-dimensional sampling.

TBSSvis: Visual Analytics for Temporal Blind Source Separation

2020

Temporal Blind Source Separation (TBSS) is used to obtain the true underlying processes from noisy temporal multivariate data, such as electrocardiograms. TBSS has similarities to Principal Component Analysis (PCA) as it separates the input data into univariate components and is applicable to suitable datasets from various domains, such as medicine, finance, or civil engineering. Despite TBSS’s broad applicability, the involved tasks are not well supported in current tools, which offer only text-based interactions and single static images. Analysts are limited in analyzing and comparing obtained results, which consist of diverse data such as matrices and sets of time series. Additionally, p…

Prediction of leukocyte counts during paediatric acute lymphoblastic leukaemia maintenance therapy

2019

Maintenance chemotherapy with oral 6-mercaptopurine and methotrexate remains a cornerstone of modern therapy for acute lymphoblastic leukaemia. The dosage and intensity of therapy are based on surrogate markers such as peripheral blood leukocyte and neutrophil counts. Dosage based leukocyte count predictions could provide support for dosage decisions clinicians face trying to find and maintain an appropriate dosage for the individual patient. We present two Bayesian nonlinear state space models for predicting patient leukocyte counts during the maintenance therapy. The models simplify some aspects of previously proposed models but allow for some extra flexibility. Our second model is an ext…

Nordic stock market integration

2001

Deflation-based separation of uncorrelated stationary time series

2014

In this paper we assume that the observed pp time series are linear combinations of pp latent uncorrelated weakly stationary time series. The problem is then to find an estimate for an unmixing matrix that transforms the observed time series back to uncorrelated time series. The so called SOBI (Second Order Blind Identification) estimate aims at a joint diagonalization of the covariance matrix and several autocovariance matrices with varying lags. In this paper, we propose a novel procedure that extracts the latent time series one by one. The limiting distribution of this deflation-based SOBI is found under general conditions, and we show how the results can be used for the comparison of es…