State-space discrimination and clustering of atmospheric time series data based on Kullback information measures

2008 ◽  
Vol 19 (2) ◽  
pp. 103-121 ◽  
Author(s):  
Thomas Bengtsson ◽  
Joseph E. Cavanaugh
Keyword(s):  
Time Series ◽  
State Space ◽  
Time Series Data ◽  
Series Data ◽  
Information Measures ◽  
Kullback Information

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.

scholarly journals Comparison of autoregressive moving average and state space methods for monthly time series modelling of Labrador and South-East Quebec river flows

2015 ◽  
Vol 51 (3) ◽  
pp. 200-218 ◽  
Author(s):  
Carissa Sparkes ◽  
Leonard M. Lye ◽  
Susan Richter
Keyword(s):  
Time Series ◽  
State Space ◽  
Time Series Data ◽  
Moving Average ◽  
Series Data ◽  
Hydrologic Modelling ◽  
Stream Flows ◽  
Time Series Modelling ◽  
High Level ◽  
Accuracy Of Prediction

Time series data such as monthly stream flows can be modelled using time series methods and then used to simulate or forecast flows for short term planning. Two methods of time series modelling were reviewed and compared: the well-known auto regressive moving average (ARMA) method and the state-space time-series (SSTS) method. ARMA has been used in hydrology to model and simulate flows with good results and is widely accepted for this purpose. SSTS modelling is a more recently developed method that is relatively unused for hydrologic modelling. This paper focuses on modelling the stream flows from basins of different sizes using these two time series modelling methods and comparing the results. Three rivers in Labrador and South-East Quebec were modelled: the Romaine, Ugjoktok and Alexis Rivers. Both models were compared for accuracy of prediction, ease of software use and simplicity of model to determine the preferred time series methodology approach for modelling these rivers. The SSTS was considered very easy to use but model diagnostics were found to require a high level of statistical understanding. Ultimately, the ARMA method was determined to be the better method for the typical engineer to use, considering the diagnostics were simple and the monthly flows could be easily simulated to verify results.

scholarly journals Stochastic Claims Reserving Methods with State Space Representations: A Review

Risks ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 198
Author(s):  
Nataliya Chukhrova ◽  
Arne Johannssen
Keyword(s):  
Time Series ◽  
State Space ◽  
Life Insurance ◽  
Time Series Data ◽  
Temporal Dynamics ◽  
Insurance Companies ◽  
Series Data ◽  
Time Series Models ◽  
Life Insurer ◽  
High Flexibility

Often, the claims reserves exceed the available equity of non-life insurance companies and a change in the claims reserves by a small percentage has a large impact on the annual accounts. Therefore, it is of vital importance for any non-life insurer to handle claims reserving appropriately. Although claims data are time series data, the majority of the proposed (stochastic) claims reserving methods is not based on time series models. Among the time series models, state space models combined with Kalman filter learning algorithms have proven to be very advantageous as they provide high flexibility in modeling and an accurate detection of the temporal dynamics of a system. Against this backdrop, this paper aims to provide a comprehensive review of stochastic claims reserving methods that have been developed and analyzed in the context of state space representations. For this purpose, relevant articles are collected and categorized, and the contents are explained in detail and subjected to a conceptual comparison.

Structural condition assessment using entropy-based time series analysis

2017 ◽  
Vol 28 (14) ◽  
pp. 1941-1956 ◽  
Author(s):  
Mehrisadat Makki Alamdari ◽  
Bijan Samali ◽  
Jianchun Li ◽  
Ye Lu ◽  
Samir Mustapha
Keyword(s):  
Time Series ◽  
State Space ◽  
Structural Damage ◽  
Time Series Data ◽  
National Laboratory ◽  
The State ◽  
Frame Structure ◽  
Series Data ◽  
Symbol Space ◽  
Symbol Sequences

We present a time-series-based algorithm to identify structural damage in the structure. The method is in the context of non-model-based approaches; hence, it eliminates the need of any representative numerical model of the structure to be built. The method starts by partitioning the state space into a finite number of subsets which are mutually exclusive and exhaustive and each subset is identified by a distinct symbol. Partitioning is performed based on a maximum entropy approach which takes into account the sparsity and distribution of information in the time series. After constructing the symbol space, the time series data are uniquely transformed from the state space into the constructed symbol space to create the symbol sequences. Symbol sequences are the simplified abstractions of the complex system and describe the evolution of the system. Each symbol sequence is statistically characterized by its entropy which is obtained based on the probability of occurrence of the symbols in the sequence. As a consequence of damage occurrence, the entropy of the symbol sequences changes; this change is implemented to define a damage indicative feature. The method shows promising results using data from two experimental case studies subject to varying excitation. The first specimen is a reinforced concrete jack arch which replicates one of the major structural components of the Sydney Harbor Bridge and the second specimen is a three-story frame structure model which has been tested at Los Alamos National Laboratory. The method not only could successfully identify the presence of damage but also has potential to localize it.

scholarly journals Quantifying non-communicable diseases’ burden in Egypt using State-Space model

PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0245642
Author(s):  
Somaya El-Saadani ◽  
Mohamed Saleh ◽  
Sarah A. Ibrahim
Keyword(s):  
Time Series ◽  
Health Services ◽  
State Space ◽  
Bayesian Methods ◽  
Time Series Data ◽  
State Space Model ◽  
Series Data ◽  
Non Communicable Diseases ◽  
Space Model ◽  
Health Burden

The study aimed to model and quantify the health burden induced by four non-communicable diseases (NCDs) in Egypt, the first to be conducted in the context of a less developing county. The study used the State-Space model and adopted two Bayesian methods: Particle Filter and Particle Independent Metropolis-Hastings to model and estimate the NCDs’ health burden trajectories. We drew on time-series data of the International Health Metric Evaluation, the Central Agency for Public Mobilization and Statistics (CAPMAS) Annual Bulletin of Health Services Statistics, the World Bank, and WHO data. Both Bayesian methods showed that the burden trajectories are on the rise. Most of the findings agreed with our assumptions and are in line with the literature. Previous year burden strongly predicts the burden of the current year. High prevalence of the risk factors, disease prevalence, and the disease’s severity level all increase illness burden. Years of life lost due to death has high loadings in most of the diseases. Contrary to the study assumption, results found a negative relationship between disease burden and health services utilization which can be attributed to the lack of full health insurance coverage and the pattern of health care seeking behavior in Egypt. Our study highlights that Particle Independent Metropolis-Hastings is sufficient in estimating the parameters of the study model, in the case of time-constant parameters. The study recommends using state Space models with Bayesian estimation approaches with time-series data in public health and epidemiology research.

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