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  • 1.
    Helske, Jouni
    University of Jyväskylä, Finland.
    KFAS: Exponential Family State Space Models in R2017Ingår i: Journal of Statistical Software, ISSN 1548-7660, E-ISSN 1548-7660, Vol. 78, nr 10Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    State space modeling is an efficient and flexible method for statistical inference of a broad class of time series and other data. This paper describes the R package KFAS for state space modeling 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 modeling is presented.

  • 2.
    Helske, Jouni
    et al.
    Department of Signal Processing, Tampere University of Technology, Tampere, FINLAND.
    Eerola, Mervi
    Methodology Centre for Human Sciences, University of Jyväskylä, FINLAND.
    Tabus, Ioan
    Department of Signal Processing, Tampere University of Technology, Tampere, FINLAND.
    Minimum description length based hidden Markov model clustering for life sequence analysis2010Ingår i: Proceedings of the Third Workshop on Information Theoretic Methods in Science and Engineering, August 16-18, 2010, Tampere, Finland, 2010Konferensbidrag (Refereegranskat)
    Abstract [en]

    In this article, a model-based method for clustering life sequences is suggested. In the social sciences, model-free clustering methods are often used in order to find typical life sequences. The suggested method, which is based on hidden Markov models, provides principled probabilistic ranking of candidate clusterings for choosing the best solution. After presenting the principle of the method and algorithm, the method is tested with real life data, where it finds eight descriptive clusters with clear probabilistic structures.

  • 3.
    Helske, Jouni
    et al.
    University of Jyväskylä, Jyväskylä, Finland.
    Nyblom, Jukka
    University of Jyväskylä, Jyväskylä, Finland.
    Improved frequentist prediction intervals for ARMA models by simulation2014Ingår i: Contributions to Mathematics, Statistics, Econometrics, and Finance: essays in honour of professor Seppo Pynnönen / [ed] Knif, Johan; Pape, Bernd, Vaasa, Finland: University of Vaasa , 2014, s. 71-86Kapitel i bok, del av antologi (Övrigt vetenskapligt)
  • 4.
    Helske, Jouni
    et al.
    University of Jyväskylä, Jyväskylä, Finland.
    Nyblom, Jukka
    University of Jyväskylä, Jyväskylä, Finland.
    Improved frequentist prediction intervals for autoregressive models by simulation2015Ingår i: Unobserved Components and Time Series Econometrics / [ed] Siem Jan Koopman and Neil Shephard, Oxford: Oxford University Press, 2015, s. 291-309Kapitel i bok, del av antologi (Övrigt vetenskapligt)
    Abstract [en]

    It is well known that the so-called plug-in prediction intervals for autoregressive processes, with Gaussian disturbances, are too short, i.e. the coverage probabilities fall below the nominal ones. However, simulation experiments show that the formulas borrowed from the ordinary linear regression theory yield one-step prediction intervals, which have coverage probabilities very close to that claimed. From a Bayesian point of view the resulting intervals are posterior predictive intervals when uniform priors are assumed for both autoregressive coefficients and logarithm of the disturbance variance. This finding enables one to see how to treat multi-step prediction intervals that are obtained by simulation either directly from the posterior distribution or using importance sampling. An application of the method to forecasting the annual gross domestic product growth in the United Kingdom and Spain is given for the period 2002 to 2011 using the estimation period 1962 to 2001.

  • 5.
    Helske, Jouni
    et al.
    Department of Mathematics and Statistics, University of Jyväskylä, Jyväskylä, Finland.
    Nyblom, Jukka
    Department of Mathematics and Statistics, University of Jyväskylä, Jyväskylä, Finland.
    Ekholm, Petri
    Department of Mathematics and Statistics, University of Jyväskylä, Jyväskylä, Finland; Finnish Environment Institute, Finland.
    Meissner, Kristian
    Department of Mathematics and Statistics, University of Jyväskylä, Jyväskylä, Finland; Finnish Environment Institute, Finland.
    Estimating aggregated nutrient fluxes in four Finnish rivers via Gaussian state space models2013Ingår i: Environmetrics, ISSN 1180-4009, E-ISSN 1099-095X, Vol. 24, nr 4, s. 237-247Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Reliable estimates of the nutrient fluxes carried by rivers from land-based sources to the sea are needed for efficient abatement of marine eutrophication. Although nutrient concentrations in rivers generally display large temporal variation, sampling and analysis for nutrients, unlike flow measurements, are rarely performed on a daily basis. The infrequent data calls for ways to reliably estimate the nutrient concentrations of the missing days. Here, we use the Gaussian state space models with daily water flow as a predictor variable to predict missing nutrient concentrations for four agriculturally impacted Finnish rivers. Via simulation of Gaussian state space models, we are able to estimate aggregated yearly phosphorus and nitrogen fluxes, and their confidence intervals.The effect of model uncertainty is evaluated through a Monte Carlo experiment, where randomly selected sets of nutrient measurements are removed and then predicted by the remaining values together with re-estimated parameters. Results show that our model performs well for rivers with long-term records of flow. Finally, despite the drastic decreases in nutrient loads on the agricultural catchments of the rivers over the last 25years, we observe no corresponding trends in riverine nutrient fluxes.

  • 6.
    Helske, Satu
    et al.
    Linköpings universitet, Institutionen för ekonomisk och industriell utveckling, Institutet för analytisk sociologi, IAS. Linköpings universitet, Filosofiska fakulteten. University of Oxford, United Kingdom; University of Jyväskylä, Finland.
    Helske, Jouni
    Linköpings universitet, Institutionen för teknik och naturvetenskap, Medie- och Informationsteknik. Linköpings universitet, Tekniska fakulteten. University of Jyväskylä, Finland.
    Mixture Hidden Markov Models for Sequence Data: The seqHMM Package in R2019Ingår i: Journal of Statistical Software, ISSN 1548-7660, E-ISSN 1548-7660, Vol. 88, nr 3, s. 32s. 1-32Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    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 covariates. The seqHMM package in R is designed for the efficient modeling of sequences and other categorical time series data containing one or multiple subjects with one or multiple interdependent sequences using HMMs and MHMMs. Also other restricted variants of the MHMM can be fitted, e.g., latent class models, Markov models, mixture Markov models, or even ordinary multinomial regression models with suitable parameterization of the HMM. Good graphical presentations of data and models are useful during the whole analysis process from the first glimpse at the data to model fitting and presentation of results. The package provides easy options for plotting parallel sequence data, and proposes visualizing HMMs as directed graphs.less thanbr /greater thanComment: 33 pages, 8 figures

  • 7.
    Helske, Satu
    et al.
    University of Jyvaskyla, Finland.
    Helske, Jouni
    University of Jyvaskyla, Finland.
    Eerola, Mervi
    University of Turku, Finland.
    Analysing Complex Life Sequence Data with Hidden Markov Modelling2016Ingår i: Proceedings of the International Con-ference on Sequence Analysis and Related Methods, Lausanne, June 8-10,2016, pp 209-240 / [ed] G. Ritschard and M. Studer, LaCOSA II , 2016Konferensbidrag (Refereegranskat)
    Abstract [en]

    When analysing complex sequence data with multiple channels (dimen- sions) and long observation sequences, describing and visualizing the data can be a challenge. Hidden Markov models (HMMs) and their mixtures (MHMMs) offer a probabilistic model-based framework where the information in such data can be compressed into hidden states (general life stages) and clusters (general patterns in life courses). We studied two different approaches to analysing clustered life sequence data with sequence analysis (SA) and hidden Markov modelling. In the first approach we used SA clusters as fixed and estimated HMMs separately for each group. In the second approach we treated SA clusters as suggestive and used them as a starting point for the estimation of MHMMs. Even though the MHMM approach has advantages, we found it to be unfeasible in this type of complex setting. Instead, using separate HMMs for SA clusters was useful for finding and describing patterns in life courses. 

  • 8.
    Helske, Satu
    et al.
    Linköpings universitet, Institutionen för ekonomisk och industriell utveckling, Institutet för analytisk sociologi, IAS. Linköpings universitet, Filosofiska fakulteten. Department of Sociology, University of Oxford, Oxford, UK / Department of Mathematics and Statistics, University of Jyvaskyla, Jyvaskyla, Finland.
    Helske, Jouni
    Linköpings universitet, Institutionen för teknik och naturvetenskap, Medie- och Informationsteknik. Linköpings universitet, Tekniska fakulteten. Department of Mathematics and Statistics, University of Jyvaskyla, Jyvaskyla, Finland.
    Eerola, Mervi
    Centre of Statistics, University of Turku, Turku, Finland.
    Combining Sequence Analysis and Hidden Markov Models in the Analysis of Complex Life Sequence Data2018Ingår i: Sequence Analysis and Related Approaches / [ed] Gilbert Ritschard, Matthias Studer, Switzerland: Springer, 2018, s. 185-200Kapitel i bok, del av antologi (Refereegranskat)
    Abstract [en]

    Life course data often consists of multiple parallel sequences, one for each life domain of interest. Multichannel sequence analysis has been used for computing pairwise dissimilarities and finding clusters in this type of multichannel (or multidimensional) sequence data. Describing and visualizing such data is, however, often challenging. We propose an approach for compressing, interpreting, and visualizing the information within multichannel sequences by finding (1) groups of similar trajectories and (2) similar phases within trajectories belonging to the same group. For these tasks we combine multichannel sequence analysis and hidden Markov modelling. We illustrate this approach with an empirical application to life course data but the proposed approach can be useful in various longitudinal problems.

  • 9.
    Lindsten, Fredrik
    et al.
    Uppsala Univ, Sweden.
    Helske, Jouni
    Linköpings universitet, Institutionen för teknik och naturvetenskap, Medie- och Informationsteknik. Linköpings universitet, Tekniska fakulteten.
    Vihola, Matti
    Univ Jyvaskyla, Finland.
    Graphical model inference: Sequential Monte Carlo meets deterministic approximations2018Ingår i: ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 31 (NIPS 2018), NEURAL INFORMATION PROCESSING SYSTEMS (NIPS) , 2018, Vol. 31Konferensbidrag (Refereegranskat)
    Abstract [en]

    Approximate inference in probabilistic graphical models (PGMs) can be grouped into deterministic methods and Monte-Carlo-based methods. The former can often provide accurate and rapid inferences, but are typically associated with biases that are hard to quantify. The latter enjoy asymptotic consistency, but can suffer from high computational costs. In this paper we present a way of bridging the gap between deterministic and stochastic inference. Specifically, we suggest an efficient sequential Monte Carlo (SMC) algorithm for PGMs which can leverage the output from deterministic inference methods. While generally applicable, we show explicitly how this can be done with loopy belief propagation, expectation propagation, and Laplace approximations. The resulting algorithm can be viewed as a post-correction of the biases associated with these methods and, indeed, numerical results show clear improvements over the baseline deterministic methods as well as over "plain" SMC.

  • 10.
    Luukko, P. J. J.
    et al.
    Univ Jyvaskyla, Finland.
    Helske, Jouni
    Univ Jyvaskyla, Finland.
    Rasanen, E.
    Tampere Univ Technol, Finland.
    Introducing libeemd: a program package for performing the ensemble empirical mode decomposition2016Ingår i: Computational statistics (Zeitschrift), ISSN 0943-4062, E-ISSN 1613-9658, Vol. 31, nr 2, s. 545-557Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    The ensemble empirical mode decomposition (EEMD) and its complete variant (CEEMDAN) are adaptive, noise-assisted data analysis methods that improve on the ordinary empirical mode decomposition (EMD). All these methods decompose possibly nonlinear and/or nonstationary time series data into a finite amount of components separated by instantaneous frequencies. This decomposition provides a powerful method to look into the different processes behind a given time series data, and provides a way to separate short time-scale events from a general trend. We present a free software implementation of EMD, EEMD and CEEMDAN and give an overview of the EMD methodology and the algorithms used in the decomposition. We release our implementation, libeemd, with the aim of providing a user-friendly, fast, stable, well-documented and easily extensible EEMD library for anyone interested in using (E)EMD in the analysis of time series data. While written in C for numerical efficiency, our implementation includes interfaces to the Python and R languages, and interfaces to other languages are straightforward.

  • 11.
    Muthumanickam, Prithiviraj
    et al.
    Linköpings universitet, Institutionen för teknik och naturvetenskap, Medie- och Informationsteknik. Linköpings universitet, Tekniska fakulteten.
    Helske, Jouni
    Linköpings universitet, Institutionen för teknik och naturvetenskap, Medie- och Informationsteknik. Linköpings universitet, Tekniska fakulteten.
    Vitoria, Aida
    Linköpings universitet, Institutionen för teknik och naturvetenskap, Medie- och Informationsteknik. Linköpings universitet, Tekniska fakulteten.
    Johansson, Jimmy
    Linköpings universitet, Institutionen för teknik och naturvetenskap, Medie- och Informationsteknik. Linköpings universitet, Tekniska fakulteten. Linköpings universitet, Centrum för klimatpolitisk forskning, CSPR.
    Cooper, Matthew
    Linköpings universitet, Institutionen för teknik och naturvetenskap, Medie- och Informationsteknik. Linköpings universitet, Tekniska fakulteten.
    Comparison of Attention Behaviour Across User Sets through Automatic Identification of Common Areas of Interest2019Konferensbidrag (Refereegranskat)
    Abstract [en]

    Eye tracking is used to analyze and compare user behaviour within numerous domains, but long duration eye tracking experiments across multiple users generate millions of eye gaze samples, making the data analysis process complex. Usually the samples are labelled into Areas of Interest (AoI) or Objects of Interest (OoI), where the AoI approach aims to understand how a user monitors different regions of a scene while OoI identification uncovers distinct objects in the scene that attract user attention. Using scalable clustering and cluster merging techniques that require minimal user input, we label AoIs across multiple users in long duration eye tracking experiments. Using the common AoI labels then allows direct comparison of the users as well as the use of such methods as Hidden Markov Models and Sequence mining to uncover common and distinct behaviour between the users which, until now, has been prohibitively difficult to achieve.

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