Search results for "aikasarja-analyysi"
showing 10 items of 20 documents
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…
National-Level Wealth Inequality and Socioeconomic Inequality in Adolescent Mental Well-Being: A Time Series Analysis of 17 Countries
2020
Purpose: Although previous research has established a positive association between national income inequality and socioeconomic inequalities in adolescent health, very little is known about the extent to which national-level wealth inequalities (i.e., accumulated financial resources) are associated with these inequalities in health. Therefore, this study examined the association between national wealth inequality and income inequality and socioeconomic inequality in adolescents' mental well-being at the aggregated level. Methods: Data were from 17 countries participating in three consecutive waves (2010, 2014, and 2018) of the cross-sectional Health Behaviour in School-aged Children study. …
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…
Disentangling conditional effects of multiple regime shifts on Atlantic cod productivity
2020
AbstractRegime shifts are increasingly prevalent in the ecological literature. However, definitions vary, and many detection methods are subjective. Here, we employ an operationally objective means of identifying regime shifts, using a Bayesian online change-point detection algorithm able to simultaneously identify shifts in the mean and(or) variance of time series data. We detected multiple regime shifts in long-term (59-154 years) patterns of coastal Norwegian Atlantic cod (>70% decline) and putative drivers of cod productivity: North Atlantic Oscillation (NAO); sea-surface temperature; zooplankton abundance; fishing mortality (F). The consequences of an environmental or climate-relate…
Dimension reduction for time series in a blind source separation context using r
2021
Funding Information: The work of KN was supported by the CRoNoS COST Action IC1408 and the Austrian Science Fund P31881-N32. The work of ST was supported by the CRoNoS COST Action IC1408. The work of JV was supported by Academy of Finland (grant 321883). We would like to thank the anonymous reviewers for their comments which improved the paper and package considerably. Publisher Copyright: © 2021, American Statistical Association. All rights reserved. Multivariate time series observations are increasingly common in multiple fields of science but the complex dependencies of such data often translate into intractable models with large number of parameters. An alternative is given by first red…
Aikasarjamallit apuna Suomen talouden seurannassa
2019
Viimeisten vuosikymmenien aikana kansainvälisessä ekonometrisessa tutkimuskirjallisuudessa on esitetty useita makrotaloudellista tilaa kuvaavien muuttujien informaatiota yhdistäviä lyhyen aikavälin mallinnus- ja ennustemenetelmiä. Näitä ns. nowcasting-menetelmiä on myös onnistuneesti hyödynnetty ja sovellettu Suomen talouden seurantaan. Tässä artikkelissa esittelemme katsauksen monella taholla tehtyyn kehitystyöhön ja näiden hankkeiden yhteydessä saatuihin tuloksiin Suomen aineiston tapauksessa. Suomen taloutta koskevien suhdanneindeksien hyödyntämisen myötä suhdanteiden käännepisteiden määrittäminen on tarkempaa ja käännepisteiden tuottamia taantumajaksoja voidaan vastaavasti ennustaa binä…
A review of second‐order blind identification methods
2022
Second order source separation (SOS) is a data analysis tool which can be used for revealing hidden structures in multivariate time series data or as a tool for dimension reduction. Such methods are nowadays increasingly important as more and more high-dimensional multivariate time series data are measured in numerous fields of applied science. Dimension reduction is crucial, as modelling such high-dimensional data with multivariate time series models is often impractical as the number of parameters describing dependencies between the component time series is usually too high. SOS methods have their roots in the signal processing literature, where they were first used to separate source sig…
Extracting Conditionally Heteroskedastic Components using Independent Component Analysis
2020
In the independent component model, the multivariate data are assumed to be a mixture of mutually independent latent components. The independent component analysis (ICA) then aims at estimating these latent components. In this article, we study an ICA method which combines the use of linear and quadratic autocorrelations to enable efficient estimation of various kinds of stationary time series. Statistical properties of the estimator are studied by finding its limiting distribution under general conditions, and the asymptotic variances are derived in the case of ARMA-GARCH model. We use the asymptotic results and a finite sample simulation study to compare different choices of a weight coef…
Poverty, inequality and the Finnish 1860s famine
2016
Otsoniaineiston analysointi lineaarisella tila-avaruusmallilla
2014
Tutkielma käsittelee yläilmakehän otsonimäärän mallintamista lineaarisella tila-avaruusmallilla. Ilmakehän otsonimäärä vaihtelee vuodenajan mukaan ja lisäksi tunnetaan joitakin luonnollisia tekijöitä, jotka vaikuttavat otsonimäärään. Tämän lisäksi erilaiset ihmisen toiminnan vuoksi ilmakehässä lisääntyneet aineet aiheuttavat muutoksia otsonimäärässä. Aiemmin otsonimäärää on mallinnettu yleistettyjen lineaaristen mallien avulla. Tila-avaruusmallit ovat kuitenkin huomattavasti monipuolisempia malleja, joilla voidaan hyvin mallintaa ajassa tapahtuvaa kehitystä. Mallin sovittamisessa käytetään modernia MCMC-algoritmia. Aineistona käytetään kahden eri satelliitti-instrumentin havaintoja otsonimä…