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Normalizing Kalman Filters for Multivariate Time Series Analysis

Emmanuel de Bézenac, Syama Sundar Rangapuram, Konstantinos Benidis, Michael Bohlke-Schneider, Richard Kurle, Lorenzo Stella, Hilaf Hasson, Patrick Gallinari, Tim Januschowski
2020 Neural Information Processing Systems  
This paper tackles the modelling of large, complex & multivariate time series panels in a probabilistic setting.  ...  By augmenting state space models with normalizing flows, we mitigate imprecisions stemming from idealized assumptions in state space models.  ...  Normalizing Kalman Filters Let y t ∈ R N denote the value of a multivariate time series at time t, with y t,i ∈ R the value of the corresponding i-th univariate time series.  ... 
dblp:conf/nips/BezenacRBBKSHGJ20 fatcat:funs4nmxzrd3vewcbzyic2yljy

WRF model input for improved radar rainfall estimates using Kalman Filter

Wardah T., Sharifah Nurul Huda S.Y., Suzana R., Hamzah A., Maisarah W.W.I
2014 2014 International Symposium on Technology Management and Emerging Technologies  
The study found that filtering technique using Kalman Filter with multivariate analysis applying the WRF model output has satisfactorily improve radar rainfall estimates.  ...  This research work was to reduce radar rainfall bias due to the process and measurement noises using Kalman Filter with a multivariate analysis technique.  ...  Kalman Filter is suitable for stationary or nonstationary time series, and also works on time series with missing values [18] .  ... 
doi:10.1109/istmet.2014.6936527 fatcat:yoj6je65pvgnvlk64r55inav7m

Page 5236 of Mathematical Reviews Vol. , Issue 86k [page]

1986 Mathematical Reviews  
Author summary: “Any multivariate ARMA model may be put in a form suitable for the application of Kalman filtering techniques.  ...  Essays in time series and allied processes. J. Appl. Probab. 1986, Special Vol. 23A, 257-272. Unlike the linear case, nonlinear time series models do not have any standard method of analysis.  ... 

Stanza: A Nonlinear State Space Model for Probabilistic Inference in Non-Stationary Time Series [article]

Anna K. Yanchenko, Sayan Mukherjee
2020 arXiv   pre-print
Time series with long-term structure arise in a variety of contexts and capturing this temporal structure is a critical challenge in time series analysis for both inference and forecasting settings.  ...  Stanza strikes a balance between competitive forecasting accuracy and probabilistic, interpretable inference for highly structured time series.  ...  Traditionally, state space models like Kalman Filters [15] , Dynamic Linear Models (DLMs) [35] and their numerous extensions [21, 8] have been utilized for probabilistic inference in time series analysis  ... 
arXiv:2006.06553v1 fatcat:exz7imft2zeizimaowywxq5z2e

Seizure-Onset Mapping Based on Time-Variant Multivariate Functional Connectivity Analysis of High-Dimensional Intracranial EEG: A Kalman Filter Approach

Octavian V. Lie, Pieter van Mierlo
2016 Brain Topography  
The aim of this study was to test two Kalman filter implementations, a well-known multivariate adaptive AR model (Arnold et al. 1998 ) and a simplified, computationally efficient derivation of it, for  ...  Due to their high computational cost, these methods have been applied to a limited number of iEEG time-series (<60).  ...  For multivariate time-series, the vector representation of a time-varying AR process is ( ) = ∑ ( ) ( − ) + ( ) =1 2 where ( ) = ( 1 ( ), 2 ( ) … ( )) for signals/channels, is the × coefficient matrix  ... 
doi:10.1007/s10548-016-0527-x pmid:27722839 fatcat:mucf2cc6i5gk7ppkufzxzqipb4

Weather event severity prediction using buoy data and machine learning [article]

Vikas Ramachandra
2019 arXiv   pre-print
Data sources for the buoys and weather events are identified, aggregated and merged. For missing data imputation, we use Kalman filters as well as splines for multivariate time series.  ...  In this paper, we predict severity of extreme weather events (tropical storms, hurricanes, etc.) using buoy data time series variables such as wind speed and air temperature.  ...  Then, we apply both univariate Kalman filter [9] and multivariate splines [10] for all variables, using data from nearby stations, for the same variable.  ... 
arXiv:1911.09001v1 fatcat:fszeazz47veybepruuyakw7zru

Page 3246 of Mathematical Reviews Vol. , Issue 2004d [page]

2004 Mathematical Reviews  
The solution is based on using the standard Kalman filter cascaded with the smoothing filter.  ...  Summary: “The analysis of extreme values within a stationary time series entails various assumptions concerning its long- and short-range dependence.  ... 

Inference of time-dependent causal influences in Networks

M. Killmann, L. Sommerlade, W. Mader, J. Timmer, B. Schelter
2012 Biomedical Engineering  
We show that the State Space Model in combination with the Kalman filter is a powerful tool for estimating time-variate AR process parameters.  ...  For time-varying connections a time-resolved estimation of the coefficients is mandatory.  ...  B.S. and L.S. are indebted to the Landesstiftung Baden-Württemberg for the financial support of this research project by the Eliteprogramme for Postdocs.  ... 
doi:10.1515/bmt-2012-4263 fatcat:usl447vourc6poac2l24v26x2a

Stochastic structures of world's death counts after World War II

Jae J. Lee
2022 Communications for Statistical Applications and Methods  
The structural time series models use the Kalman filter to estimate unknown parameters of a stochastic model, to predict future data, and to do filtering data.  ...  The structural time series models assume that a time series can be formulated directly with the unobserved components such as trend, slope, seasonal, cycle and daily effect.  ...  Harrison and Stevens (1971) was the first paper to apply the Kalman filter to a time series analysis.  ... 
doi:10.29220/csam.2022.29.3.353 fatcat:7wkyag2sbvdxjkyqzkwckw6t6i

Bounded-observation Kalman filtering of correlation in multivariate neural recordings

Mohammad Mehdi Kafashan, Ben J. Palanca, ShiNung Ching
2014 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society  
Here, we present a simple, data-driven Kalman filter-based approach to tracking correlation dynamics.  ...  The filter explicitly accounts for the bounded nature of correlation measurements through the inclusion of a Fisher transform in the measurement equation.  ...  The filter operates on the time series constructed by computing correlations in successive bins.  ... 
doi:10.1109/embc.2014.6944760 pmid:25571128 pmcid:PMC5296652 dblp:conf/embc/KafashanPC14 fatcat:tjvlwexsmbc7voydfda4k674vu

A Comparison of Predictive Parameter Estimation using Kalman Filter and Analysis of Variance

Asim ur, Haider Mehdi, Syed Muhammad, Muhammad Junaid
2017 International Journal of Advanced Computer Science and Applications  
This paper compares the prediction of system states using Kalman filter and a novel approach analysis of variance (ANOVA). Kalman filter has been successfully applied in several applications.  ...  A significant advantage of Kalman filter is its ability to use system output to predict the future states.  ...  The implementation of Kalman filter observer for multivariable ship control system is discussed in [2] .  ... 
doi:10.14569/ijacsa.2017.080857 fatcat:ps2ucmn2andihnnoxh2xiry2ve

Fast Filtering and Smoothing for Multivariate State Space Models

S. J. Koopman, J. Durbin
2000 Journal of Time Series Analysis  
This paper gives a new approach to diffuse filtering and smoothing for multivariate state space models.  ...  This strategy leads to computationally efficient methods for multivariate filtering and smoothing.  ...  Analysis based on these models is important in many areas and particularly in applied time series analysis.  ... 
doi:10.1111/1467-9892.00186 fatcat:s5bsfj2shnfwvjg7kk2snk72su

On-line novelty detection using the Kalman filter and extreme value theory

Hyoung-joo Lee, Stephen J. Roberts
2008 Pattern Recognition (ICPR), Proceedings of the International Conference on  
Our approach is based on a Kalman filter in order to model time-series data and extreme value theory is used to compute a novelty measure in a principled manner.  ...  This paper proposes an on-line (causal) novelty detection method capable of detecting both outliers and regime change points in sequential time-series data.  ...  Conclusion This paper proposes an on-line novelty detection approach for time-series data, by employing a combined Kalman filter and extreme value theory framework.  ... 
doi:10.1109/icpr.2008.4761918 dblp:conf/icpr/LeeR08 fatcat:seiettgumreafmadmiegtihlcm

Page 2607 of Mathematical Reviews Vol. , Issue 88e [page]

1988 Mathematical Reviews  
The main assumptions are that the time series is an autoregression of known order p, that the time series has a normal marginal distribution, and that there are up to s outliers occurring at known time  ...  2607 88e:62217 62M10 62M07 Schmid, Wolfgang (D-ULM) The multiple outlier prsblem in time series analysis. Austral. J. Statist. 28 (1986), no. 3, 400-413.  ... 

Online Time Series Anomaly Detection with State Space Gaussian Processes [article]

Christian Bock and François-Xavier Aubet and Jan Gasthaus and Andrey Kan and Ming Chen and Laurent Callot
2022 arXiv   pre-print
For high-dimensional time series, we propose an extension of Gaussian process factor analysis to identify the common latent processes of the time series, allowing us to detect anomalies efficiently in  ...  We propose r-ssGPFA, an unsupervised online anomaly detection model for uni- and multivariate time series building on the efficient state space formulation of Gaussian processes.  ...  These statistical robustification approaches [26] are essential to time series AD and were investigated in the context of Kalman filtering before.  ... 
arXiv:2201.06763v1 fatcat:pggomfyyivhyjncd3gjg2qfrni
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