State-space Modeling Approach Research Articles

A state-space relational event modeling approach for learning dynamic social interaction behavior

Relational event models (REMs) are the primary choice for the analysis of relational-event network data. However, the standard REM assumes static parameters, which hinders the modeling of time-varying dynamics. This assumption might be too restrictive in real-life scenarios, making a model that allows for time-varying parameters more valuable. We introduce a state-space extension of the relational event model as a way to tackle this problem. The model has three main attributes. First, it provides a statistical framework of the temporal change of the parameters. Second, it enables the forecasting of future parameter values (which can be utilized to simulate new networks that can account for temporal dynamics in out-of-sample predictions). Third, it requires smaller data structures to be loaded into computer memory compared to the standard REM; this makes the model easily scalable to large networks. We conduct empirical analyses on bike-sharing data, corporate communications, and interactions among socio-political actors to illustrate model usage and applicability.

The Impacts of COVID-19 Lockdowns on Road Transport Air Pollution in London: A State-Space Modelling Approach

The Impacts of COVID-19 Lockdowns on Road Transport Air Pollution in London: A State-Space Modelling Approach

State-Space Modeling Approach to Exploring the Index of Production in Construction for Türkiye

In recent years, researchers and statisticians have increasingly used econometric techniques to generate timely, high-quality, and detailed official statistics. This study presents a modified form of regression-based temporal disaggregation model to compile the index of production in construction for Türkiye, employing a state-space modeling approach. The model incorporates the twelve-month moving sum of deflated turnover as an observed variable and the number of employees as an exogenous variable. Finally, four alternative models—three assuming constant labor productivity and one assuming time-varying labor productivity—are compared.

Spectral dynamic causal modeling: A didactic introduction and its relationship with functional connectivity.

We present a didactic introduction to spectral dynamic causal modeling (DCM), a Bayesian state-space modeling approach used to infer effective connectivity from noninvasive neuroimaging data. Spectral DCM is currently the most widely applied DCM variant for resting-state functional MRI analysis. Our aim is to explain its technical foundations to an audience with limited expertise in state-space modeling and spectral data analysis. Particular attention will be paid to cross-spectral density, which is the most distinctive feature of spectral DCM and is closely related to functional connectivity, as measured by (zero-lag) Pearson correlations. In fact, the model parameters estimated by spectral DCM are those that best reproduce the cross-correlations between all measurements-at all time lags-including the zero-lag correlations that are usually interpreted as functional connectivity. We derive the functional connectivity matrix from the model equations and show how changing a single effective connectivity parameter can affect all pairwise correlations. To complicate matters, the pairs of brain regions showing the largest changes in functional connectivity do not necessarily coincide with those presenting the largest changes in effective connectivity. We discuss the implications and conclude with a comprehensive summary of the assumptions and limitations of spectral DCM.

Testing unit root non-stationarity in the presence of missing data in univariate time series of mobile health studies.

The use of digital devices to collect data in mobile health studies introduces a novel application of time series methods, with the constraint of potential data missing at random or missing not at random (MNAR). In time-series analysis, testing for stationarity is an important preliminary step to inform appropriate subsequent analyses. The Dickey-Fuller test evaluates the null hypothesis of unit root non-stationarity, under no missing data. Beyond recommendations under data missing completely at random for complete case analysis or last observation carry forward imputation, researchers have not extended unit root non-stationarity testing to more complex missing data mechanisms. Multiple imputation with chained equations, Kalman smoothing imputation, and linear interpolation have also been used for time-series data, however such methods impose constraints on the autocorrelation structure and impact unit root testing. We propose maximum likelihood estimation and multiple imputation using state space model approaches to adapt the augmented Dickey-Fuller test to a context with missing data. We further develop sensitivity analyses to examine the impact of MNAR data. We evaluate the performance of existing and proposed methods across missing mechanisms in extensive simulations and in their application to a multi-year smartphone study of bipolar patients.

Maximising the value of transmitted data from PSATs tracking marine fish: a case study on Atlantic bluefin tuna

BackgroundThe use of biologging tags to answer questions in animal movement ecology has increased in recent decades. Pop-up satellite archival tags (PSATs) are often used for migratory studies on large fish taxa. For PSATs, movements are normally reconstructed from variable amounts of transmitted data (unless tags are recovered, and full data archives accessed) by coupling geolocation methods with a state-space modelling (SSM) approach. Between 2018 and 2019, we deployed Wildlife Computers PSATs (MiniPATs) from which data recovery varied considerably. This led us to examine the effect of PSAT data volume on SSM performance (i.e., variation in reconstructed locations and their uncertainty). We did this by comparing movements reconstructed using partial (< 100%) and complete (100%) geolocation data sets from PSATs and investigated the variation in Global Position Estimator 3 (GPE3; Wildlife Computers’ proprietary light-based geolocation SSM) reconstructed locations and their certainty in relation to data volume and movement type (maximum dispersal distance).ResultsIn this analysis, PSATs (n = 29) deployed on Atlantic bluefin tuna (Thunnusthynnus) transmitted data after detaching from study animals for between 0.3 and 10.8 days (mean 4.2 ± 3 days), yielding between 2 and 82% (mean 27% ± 22%) of total geolocation data. The volume of geolocation data received was positively related to the amount of time a tag transmitted for and showed a weak negative relationship to the length of the tag deployment. For 12 recovered PSATs (i.e., 100% of geolocation data; mean ± 1 S.D. = 301 ± 90 days of data per fish), (i) if ABT travelled short-distances (< 1000 km), movements reconstructed from partial data sets were more similar to their complete data set counterpart than fish that travelled over longer distances (> 1000 km); (ii) for fish that travelled long distances, mean distance of locations from corresponding complete data set locations were inversely correlated with the volume of data received; (iii) if only 5% of data was used for geolocation, reconstructed locations for long-distance fish differed by 2213 ± 647 km from the locations derived from complete data sets; and, (iv) track reconstructions omitted migrations into the Mediterranean Sea if less than 30% of data was used for geolocation.ConclusionsFor Wildlife Computers MiniPATs in our specific application, movements reconstructed with as little as 30% of the total geolocation data results in plausible outputs from the GPE3. Below this data volume, however, significant differences of more than 2000 km can occur. Whilst for a single species and manufacturer, this highlights the importance of careful study planning and the value of conducting study-specific sensitivity analysis prior to inclusion of modelled locations in research outputs. Based on our findings, we suggest general steps and refinements to maximise the value of light geolocation data from PSATs deployed on aquatic animals and highlight the importance of conducting data sensitivity analyses.

State-Space Modeling, Design, and Analysis of the DC-DC Converters for PV Application: A Review

Small-signal models of dc-dc converters are often designed using a state-space averaging approach. This design can help discuss and derive the control-oriented and other frequency-domain attributes, such as input or output impedance parameters. This paper aims to model the dc-dc converters for PV application by employing a capacitor on the input side. The modeling, design, and analysis of the dc-dc converters regarding the input capacitor is limited in the literature. Five dc-dc converters, including buck, boost, buck-boost, ĆUK, and SEPIC converters, are designed and implemented using the state-space average modeling approach in MATLAB/Simulink. The circuit topology of each converter and the state-space matrices are derived considering every constraint. A rigorous and compelling analysis of the dc-dc converters is carried out to compare system stability and, ultimately, the dynamic performance. The output of the resulting small-signal models has been demonstrated in the time-domain against topology simulations. All the converters are exposed to unpredictable weather conditions and the simulations are carried out in the PSIM software. The perturb and observe (P&amp;O) maximum power point tracking (MPPT) algorithm is applied in all the converters to ensure maximum power point (MPP) achievement. The results showcase that the boost converter outperforms all other converters in terms of stability, settling time, and overshoot.

An Investigation of Stability, Controllability and Observability of a Three Degree of Freedom Translational Mechanical System using State Space Approach

State-space modelling approach which is essentially time-domain developed in the late 1960s is a new approach to the analysis and design of complex control systems. Several researches have carried out modelling and analysis of mechanical systems with a number of degrees of freedom of movement using state space approach among which is the work of Sivak and Darina (2012) who modelled a system with two degrees of freedom. In this paper, therefore, we provide an extension of the work of Sivak and Darina to model and analyse a three degree of freedom translational mechanical system using state-space approach. The system was first presented in equivalent free body diagrams, then Newton's second law of motion was used to derive its equations of motion. The State-space formulation in the controllable canonical form obtained from the time-domain differential equations is adopted and the Laplace transform method was used in the analysis to determine the poles (natural frequencies) of the system using two numerical examples in MATLAB software. The software was also used to determine the controllability and observability matrices of the system. The stability, controllability and observability of the system were then discussed from the poles, controllability matrix and observability matrix respectively. Tables of numerical values and line graphs were used to present the results obtained from the analysis. Finally, in these results, the system was found to be stable, controllable and observable suggesting that a state feedback ( pole placement ) control design for the system is possible.

State-space modeling approach for fringe pattern demodulation.

A spatial carrier fringe demodulation technique is proposed based on a state-space modeling approach for phase estimation. The fringe background intensity, carrier frequency, and phase quadrature components are considered to be the elements of the state vector, which are estimated simultaneously. The state estimation is performed using the extended Kalman filter. The simulation and experimental results are provided to demonstrate the performance comparison of the proposed method with popular and state-of-the-art methods in terms of noise robustness and phase estimation accuracy.

Multiple-Input-Single-Output prediction models of crowd dynamics for Model Predictive Control (MPC) of crowd evacuations

Predicting crowd dynamics in real-time may allow the design of adaptive pedestrian flow control mechanisms that prioritize attendees’ safety and overall experience. Single-Input-Single-Output (SISO) AutoRegresive eXogenous (ARX) prediction models of crowd dynamics have been effectively used in Linear Model Predictive Controllers (MPC) that adaptively regulate the movement of people to avoid overcrowding. However, an open research question is whether Multiple-Input, State-space, and Nonlinear modeling approaches may improve MPC control performance through better prediction capabilities. This paper considers a simulated controlled evacuation scenario, where evacuees in a long corridor dynamically receive speed instructions to modulate congestion at the exits. We aim to investigate Multiple-Input-Single-Output (MISO) prediction models such that the inputs are the control action (speed recommendation) and pedestrian flow measurement, and the output is the local density of the pedestrian outflow. State-space and Input–output MISO models, linear and neural, are identified using a data-driven approach in which input–output datasets are generated from strategically designed microscopic evacuation simulations. Different estimation algorithms, including the subspace method, prediction error minimization, and regularized AutoRegressive eXogenous (ARX) model reduction, are evaluated and compared. Finally, to investigate the importance of measuring and modeling the pedestrian inflow, the case in which the models’ structure is defined as a Single-Input-Single-Output (SISO) system has been explored, where the pedestrian inflow is considered an unmeasured input disturbance. This study has important implications for the design of more effective MPC controllers for regulating pedestrian flows. We found that the prediction error minimization algorithm performs best and that nonlinear state-space modeling does not improve prediction performance. The study suggests that modeling the inner state of the evacuation process through a state-space model positively influences predicting system dynamics. Also, modeling pedestrian inflow improves prediction performance from a predefined prediction horizon value. Overall, linear state-space models have been deemed the most suitable option in corridor-type scenarios.

Closed-Loop Neurostimulation for Biomarker-Driven, Personalized Treatment of Major Depressive Disorder.

Deep brain stimulation involves the administration of electrical stimulation to targeted brain regions for therapeutic benefit. In the context of major depressive disorder (MDD), most studies to date have administered continuous or open-loop stimulation with promising but mixed results. One factor contributing to these mixed results may stem from when the stimulation is applied. Stimulation administration specific to high-symptom states in a personalized and responsive manner may be more effective at reducing symptoms compared to continuous stimulation and may avoid diminished therapeutic effects related to habituation. Additionally, a lower total duration of stimulation per day is advantageous for reducing device energy consumption. This protocol describes an experimental workflow using a chronically implanted neurostimulation device to achieve closed-loop stimulation for individuals with treatment-refractory MDD. This paradigm hinges on determining a patient-specific neural biomarker that is related to states of high symptoms and programming the device detectors, such that stimulation is triggered by this read-out of symptom state. The described procedures include how to obtain neural recordings concurrent with patient symptom reports, how to use these data in a state-space model approach to differentiate low- and high-symptom states and corresponding neural features, and how to subsequently program and tune the device to deliver closed-loop stimulation therapy.

Intentional and spurious herding behavior: A sentiment driven analysis

By using several Thomson Reuters MarketPsych Indices, this paper explores the nexus between investors’ sentiments and herding behavior in the U.S. and Europe stock markets from January 2005 to June 2021. We apply the state–space model approach of Hwang and Salmon (2004), controlling for changes in investors’ emotionality, and document that herding is a persistent phenomenon in both markets. These effects remain robust when using the alternative methodology of Chang et al. (2004). Moreover, we find evidence of herding behavior under both extreme positive and negative sentiments, with a conspicuous effect on euphoria days, particularly in the U.S. market.

Forecasting temperature of the Saudi Arabian Province of Makkah using a discrete state–space modeling approach

The maximum and minimum air temperature components (Tmax and Tmin) play a crucial role in science. This study proposes a discrete-time identified state-space modeling approach in which the temperature fluctuation was modelled as a state-space system with the temperature time series as inputs. We aim to provide a tool for projecting future scenarios of Tmax and Tmin. The current research employs a prediction-focused methodology to system identification, with the overarching goal of developing a realistic and dynamic system model. Data on the Tmin and Tmax recorded in the Saudi Arabian province of Makkah are used to test the accuracy and robustness of the proposed methodology. The proposed model was developed utilizing 120 years' (1901–2020) worth of historical monthly time series data on Tmax and Tmin. It was applied to anticipate future temperatures over the ensuing 60 years (up to 2080). For maximum temperature projections, the fit to the data or prediction focus was 87.04% and 85.14%, respectively for the identification (training) and validation phases of the model development. Akaike’s Final Prediction Error (FPE) and Mean Squared Error (MSE) values were observed to be 0.37 °C and 0.34 °C, respectively. The prediction focus during the identification and validation phases were 86.25% and 84.78%, respectively for the minimum temperature projection. The FPE and MSE values were 0.41 °C and 0.37 °C, respectively for this instance. The findings demonstrate that the recommended discrete state-space modeling approach may be utilized to predict temperature variations in the future.

Modelling of piano string vibration using coupled mobilities and a state-space approach

In musical acoustics a double decay can occur if there are two decay slopes in the sound envelope with the second part of the time history having a less pronounced slope than the first. In piano strings the double decay can be caused by the differences between the decays of vibration in the two transverse directions of a single string: normal and parallel to the soundboard. Herein we aim to reproduce this phenomenon by coupling a stiff string model and the soundboard, in both frequency and time domain, using mobility coupling and state-space model approaches. The dynamics of a stiff string tensioned between the two end pins but disconnected from the soundboard bridge is first studied. The two transverse directions of vibration are assumed to be uncoupled due to symmetry. On the contrary, the soundboard mobilities at the point of connection with the string can be represented by a multidimensional (2x2 in this work) and fully populated matrix implying coupling between different directions. The string and the soundboard will be connected by means of frequency and time domain models to show the importance of the cross-mobility terms on the vibration response and double decay of the string.

Forest stand-by-environment interaction invalidates the use of space-for-time substitution for site index modeling under climate change

Quantification and prediction of forest site productivity potential under climate changes are core issues of growth and yield modelling and crucial for the effective management of forest stands. Climatic factors typically enter these models as long-term baseline data of mean air temperature and sum of precipitation. Such models frequently are parameterized using spatially distributed data, an approach that can be considered as indirect space-for-time substitution (SFTS), which is based on the assumption that the growth response across spatial environmental gradients is equivalent to the dynamic growth response in time when site factors shift in situ due to environmental changes over time. In site index modelling this assumption has never been thoroughly tested. We applied procedures to test the validity of the SFTS approach using altitudinal transect analysis of data from long-term experimental plots with Norway spruce (Picea abies) in southwest Germany. The following procedures were applied 1. Partitioning of spatial and temporal variance components of site index and air temperature; 2. Test of the equivalence of spatial and temporal variation in models predicting site index from air temperature; 3. Bias analysis of the SFTS- and the altitudinal range-specific individual models. The results show that the response of site index to changes in growing season air temperature as estimated by the SFTS-based approach is significantly different from the observed temporal dynamics within the plots. The estimated sensitivity of site index to changes in growing season air temperature is significantly larger in the SFTS-model. Bias trends increase with increasing air temperature in all altitudinal ranges. We found significant effects of stand-by-environment interaction on site index responses to changing growing season air temperature. Possible reasons for the found differences are discussed. Furthermore, based on an independent data set from the German National Forestry Inventory (NFI), a SFTS-based climate-sensitive site index model was compared to a state-space modelling approach (SSM) which considers the dynamics of environmental changes over time. The results show that the SSM is statistically superior to the SFTS approach, corroborating the findings of the altitudinal transect analysis. Finally, future site index dynamics of Norway spruce were simulated on the basis of climate change scenarios using the SSM approach. We recommend for all climate sensitive forest growth and yield models based on spatially and temporally distributed data to properly account for within- and between-group effects to avoid erroneous model predictions under climate change.

DIAGNOSTICS OF AN ELECTRIC MOTOR BASED ON A STATE-SPACE MODEL

This paper develops a model for DC motor diagnostics based on a state-space model approach. Its digital twin works in parallel with the real motor. The output signals of the motor and the digital twin from the electric current and angular velocity sensors are analyzed. The motor defects are detected by the magnitude of the discrepancy. If a defect is detected, the diagnostic system transmits information about it to the control system, turns on the defect indication, then by fuzzy logic methods or other methods determines the type of defect, the system element in which the defect occurred, predicts the remaining life, the control system is fail-safe operation. If the magnitude of the defect is insignificant, then appropriate maintenance actions are recommended. If the magnitude of the defect is significant, an emergency shutdown of the motor must be performed.

Dynamic Species Distribution Modeling Reveals the Pivotal Role of Human-Mediated Long-Distance Dispersal in Plant Invasion

Simple SummaryUnderstanding biological invasion mechanisms is crucial to design effective management strategies preventing their impacts on ecosystems. If the role of long-distance dispersal and age-dependent fecundity in plant invasion speed has been characterized in theory, empirical support is still rare given the difficulty to jointly fit demographic and spread parameters of dynamic models to available data. We proposed a statistical model to fit such parameters to heterogeneous observations collected across space and time. We can directly test hypotheses on the importance of various mechanisms in a past invasion. We demonstrated the potential of this method by determining the roles of human-mediated long-distance dispersal and age-dependent fecundity in the invasion of the shrub Plectranthus barbatus in South Africa. Our model revealed a massive wave of spread driven by human-mediated long-distance dispersal and originating from the cities of first introduction. The species completed its invasion of all favorable areas a few years after, in the mid-1990s. Without human-mediated long-distance dispersal, the maximum population would have been obly 30% of the current population. The delayed reproductive maturity explained the invasion lag phase. It highlights the importance of early eradication of weedy horticultural alien plants around urban areas to hamper the invasive spread.Plant invasions generate massive ecological and economic costs worldwide. Predicting their spatial dynamics is crucial to the design of effective management strategies and the prevention of invasions. Earlier studies highlighted the crucial role of long-distance dispersal in explaining the speed of many invasions. In addition, invasion speed depends highly on the duration of its lag phase, which may depend on the scaling of fecundity with age, especially for woody plants, even though empirical proof is still rare. Bayesian dynamic species distribution models enable the fitting of process-based models to partial and heterogeneous observations using a state-space modeling approach, thus offering a tool to test such hypotheses on past invasions over large spatial scales. We use such a model to explore the roles of long-distance dispersal and age-structured fecundity in the transient invasion dynamics of Plectranthus barbatus, a woody plant invader in South Africa. Our lattice-based model accounts for both short and human-mediated long-distance dispersal, as well as age-structured fecundity. We fitted our model on opportunistic occurrences, accounting for the spatio-temporal variations of the sampling effort and the variable detection rates across datasets. The Bayesian framework enables us to integrate a priori knowledge on demographic parameters and control identifiability issues. The model revealed a massive wave of spatial spread driven by human-mediated long-distance dispersal during the first decade and a subsequent drastic population growth, leading to a global equilibrium in the mid-1990s. Without long-distance dispersal, the maximum population would have been equivalent to 30% of the current equilibrium population. We further identified the reproductive maturity at three years old, which contributed to the lag phase before the final wave of population growth. Our results highlighted the importance of the early eradication of weedy horticultural alien plants around urban areas to hamper and delay the invasive spread.

Short-term forecast improvement of maximum temperature by state-space model approach: the study case of the TO CHAIR project

Short-term forecast improvement of maximum temperature by state-space model approach: the study case of the TO CHAIR project

Testing for the uncovered interest parity condition in a small open economy: A state space modelling approach

Do interest rate differentials smoothly mirror the changes in the exchange rate between a small open economy and a large emerging market economy? The literature provides conflicting views on the validity of the uncovered interest parity condition (UIP), including its size and factors influencing the risk premium. We examine the validity of the UIP condition between Nepal and India using time-series data covering the period 1989 – 2019. A state space modelling approach based on the Kalman filter analysis is applied to simulate the risk premium. We find that the UIP condition does not hold for the Nepalese Rupee. A time-varying persistent negative risk premium that dominantly explains interest rate differentials is, instead, found. These findings imply that Nepalese residents prefer to hold foreign assets and continually expect future devaluations of the domestic currency. These present obstacles to developing domestic financial markets and the implementation of a market oriented monetary policy.

Predicting political violence using a state-space model

We provide a proof-of-concept for a novel state-space modelling approach for predicting monthly deaths due to political violence. Attention is focused on developing the method and demonstrating the utility of this approach, which provides exciting opportunities to engage with domain experts in developing new and improved state-space models for predicting violence. The prediction is made on a grid of cells with spatial resolution of 0.5 × 0.5 degrees, and each cell is modeled to have two mathematically well-defined unobserved/latent/hidden states that evolves over time and encode the “onset risk” and “potential severity”, respectively. This offers a certain level of interpretability of the model. By using the model for computing the probability distribution for a death count at a future time conditioned on all data observed up until the current time, a predictive distribution is obtained. The predictive distribution typically places a certain mass at the death count 0 (no violent outbreak) and the remaining mass indicating a likely interval of the fatality count, should a violent outbreak appear. To evaluate the model performance we—lacking a better alternative—report the mean of the predictive distribution, but the access to the predictive distribution is in itself an interesting contribution to the application. This work merely serves as a proof-of-concept for the state-space modeling approach for this type of data and several possible directions for further work that could improve the predictive performance are suggested.