Time Series Analysis for the State-Space Model with R/Stan

2021 ◽  
Author(s):  
Junichiro Hagiwara
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
State Space ◽  
State Space Model ◽  
The State ◽  
Space Model ◽  
Series Analysis

scholarly journals Vibration-based damage detection in structures using time series analysis

2006 ◽  
Vol 220 (3) ◽  
pp. 261-272 ◽  
Author(s):  
I Trendafilova
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
State Space ◽  
The State ◽  
Statistical Characteristics ◽  
Space Representation ◽  
Test Case ◽  
State Space Representation ◽  
Series Analysis ◽  
Concrete Plate

The paper considers some possibilities to use pure time series analysis for damage diagnosis in vibrating structures. It introduces the basics of the state space methodology and discusses a number of possible methods to extract damage sensitive features from the state space representation of the attractor of a vibrating system. The discussed methods can be divided into two groups: methods that use non-linear dynamics characteristics and methods based on the statistical characteristics of the distribution of points on the attractor. Each possible damage feature is introduced separately and the advantages and shortfalls of its application are discussed. The application of the suggested techniques is demonstrated on a test case of a reinforced concrete plate.

scholarly journals The Promise of the State Space Approach to Time Series Analysis for Nursing Research

2012 ◽  
Vol 61 (6) ◽  
pp. 388-394 ◽  
Author(s):  
Janet A. Levy ◽  
Heather E. Elser ◽  
Robin B. Knobel
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
State Space ◽  
The State ◽  
Nursing Research ◽  
State Space Approach ◽  
Series Analysis ◽  
Space Approach

scholarly journals Modelling and Forecasting Climate Time Series with State-Space Model

2020 ◽  
pp. 149-159
Author(s):  
A. F. Adedotun ◽  
T. Latunde ◽  
O. A. Odusanya
Keyword(s):  
Time Series ◽  
State Space ◽  
Goodness Of Fit ◽  
Likelihood Function ◽  
State Space Model ◽  
The State ◽  
Initial Time ◽  
Climatic Data ◽  
Periodic Patterns ◽  
Space Model

This study modelled and estimated climatic data using the state-space model. The study was specifically to identify the pattern of the trend movement i.e., increase or decrease in the occurrence of the climatic change; to use of Univariate Kalman Filter for the computation of the likelihood function for climatic projections; to modelling the climatic dataset using the state-space model and to assess the forecasting power of the state-space models. The data used for the work includes temperature and rainfall for periods January 1991 to December 2017. The data are tested for normality. Shapiro-Wilk, Anderson-Darling and Kolmogorov-Smirnov test of normality for the climatic data all showed that the variables are not normally distributed. The work spans the use of breaking trend regression model to fit climatic data to estimate the slopes which show much increase in climatic data has been recorded from the initial time data collection until the present. Investigations and diagnostic are carried out by checking for corrections in the residuals and also checking for periodicity in the residuals. The results of this investigation show significant autocorrelation in the residuals indicating the presence of underlying noise terms which is not accounted for. By treating the residual as an autoregressive moving average (ARMA) process whereby we can obtain its spectral density, the result from the parametric spectral estimate shows underlying periodic patterns for monthly data, thus, leads to a discussion on the need to treat climatic data as a structural time series model. We select appropriate models by considering the goodness of fit of the model by comparing the Akaike information criterion (AIC) values. Parameters are estimated and accomplished with some measures of precision.

Modeling of CCLP in koryo extract production

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Ji Chol ◽  
Ri Jun Il
Keyword(s):  
Process Control ◽  
Medical Care ◽  
State Space ◽  
State Space Model ◽  
The State ◽  
Space Model ◽  
Effective Components ◽  
Counter Current

Abstract The modeling of counter-current leaching plant (CCLP) in Koryo Extract Production is presented in this paper. Koryo medicine is a natural physic to be used for a diet and the medical care. The counter-current leaching method is mainly used for producing Koryo medicine. The purpose of the modeling in the previous works is to indicate the concentration distributions, and not to describe the model for the process control. In literature, there are no nearly the papers for modeling CCLP and especially not the presence of papers that have described the issue for extracting the effective components from the Koryo medicinal materials. First, this paper presents that CCLP can be shown like the equivalent process consisting of two tanks, where there is a shaking apparatus, respectively. It allows leachate to flow between two tanks. Then, this paper presents the principle model for CCLP and the state space model on based it. The accuracy of the model has been verified from experiments made at CCLP in the Koryo Extract Production at the Gang Gyi Koryo Manufacture Factory.

scholarly journals Vitamin D, SARS-CoV-2 and Causal Associations in Transversal Studies: The Time-Series Analysis to Reveal Potential Confounders. Comment on Gaudio et al. Vitamin D Levels Are Reduced at the Time of Hospital Admission in Sicilian SARS-CoV-2-Positive Patients. Int. J. Environ. Res. Public Health 2021, 18, 3491

2021 ◽  
Vol 18 (13) ◽  
pp. 6793
Author(s):  
Cristiano Ialongo ◽  
Antonella Farina ◽  
Raffaella Labriola ◽  
Antonio Angeloni ◽  
Emanuela Anastasi
Keyword(s):  
Public Health ◽  
Vitamin D ◽  
Time Series ◽  
Severe Acute Respiratory Syndrome ◽  
Time Series Analysis ◽  
Hospital Admission ◽  
The State ◽  
Series Analysis ◽  
Vitamin D Levels ◽  
Time Of Admission

We read with great interest the paper by Gaudio and colleagues on vitamin D and on the state of patients with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) at the time of admission [...]

Estimating long-term tree mortality rate time series by combining data from periodic inventories and harvest reports in a Bayesian state-space model

2013 ◽  
Vol 292 ◽  
pp. 64-74 ◽  
Author(s):  
Katalin Csilléry ◽  
Maëlle Seignobosc ◽  
Valentine Lafond ◽  
Georges Kunstler ◽  
Benoît Courbaud
Keyword(s):  
Time Series ◽  
Mortality Rate ◽  
State Space ◽  
Tree Mortality ◽  
State Space Model ◽  
Space Model ◽  
Combining Data

Outlier detection in the state space model

1994 ◽  
Vol 20 (2) ◽  
pp. 143-148 ◽  
Author(s):  
Siddhartha Chib ◽  
Ram C. Tiwari
Keyword(s):  
State Space ◽  
Outlier Detection ◽  
State Space Model ◽  
The State ◽  
Space Model

scholarly journals LSTM-Guided Coaching Assistant for Table Tennis Practice

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4112 ◽  
Author(s):  
Se-Min Lim ◽  
Hyeong-Cheol Oh ◽  
Jaein Kim ◽  
Juwon Lee ◽  
Jooyoung Park
Keyword(s):  
Time Series ◽  
State Space ◽  
Time Series Data ◽  
State Space Model ◽  
Skill Assessment ◽  
Series Data ◽  
High Dimensional ◽  
Table Tennis ◽  
Space Model ◽  
Low Dimensional

Recently, wearable devices have become a prominent health care application domain by incorporating a growing number of sensors and adopting smart machine learning technologies. One closely related topic is the strategy of combining the wearable device technology with skill assessment, which can be used in wearable device apps for coaching and/or personal training. Particularly pertinent to skill assessment based on high-dimensional time series data from wearable sensors is classifying whether a player is an expert or a beginner, which skills the player is exercising, and extracting some low-dimensional representations useful for coaching. In this paper, we present a deep learning-based coaching assistant method, which can provide useful information in supporting table tennis practice. Our method uses a combination of LSTM (Long short-term memory) with a deep state space model and probabilistic inference. More precisely, we use the expressive power of LSTM when handling high-dimensional time series data, and state space model and probabilistic inference to extract low-dimensional latent representations useful for coaching. Experimental results show that our method can yield promising results for characterizing high-dimensional time series patterns and for providing useful information when working with wearable IMU (Inertial measurement unit) sensors for table tennis coaching.

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