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.

Short-Term Oil Price Forecasting Based on State Space Model

2011 ◽  
Vol 403-408 ◽  
pp. 2530-2534
Author(s):  
Wei Qi Li ◽  
Lin Wei Ma ◽  
Ya Ping Dai ◽  
Dong Hai Li
Keyword(s):  
State Space ◽  
Likelihood Function ◽  
State Space Model ◽  
The State ◽  
Oil Price ◽  
Filter Method ◽  
Parameter Estimates ◽  
Price Forecasting ◽  
Short Term ◽  
Space Model

In competitive petroleum markets, oil price forecasting has always been an important strategic tool for oil producers and consumers to predict market behavior. In this study, we researched the monthly crude oil price in the period between 1988 and 2009. Firstly, we present a state space model to represent oil price system. Secondly, we determine the parameter estimates of the state space model for oil price through a faster algorithm to compute the likelihood function. Lastly, we use the Kalman filter method to estimate the next three months’ oil price and compare it with the econometric structure model as a benchmark. Empirical results indicate that the state space model performs well in terms of some standard statistics indices, and it may be a promising method for short-term oil price forecasting.

scholarly journals State-Space Algorithms for Estimating Spike Rate Functions

2010 ◽  
Vol 2010 ◽  
pp. 1-14 ◽  
Author(s):  
Anne C. Smith ◽  
Joao D. Scalon ◽  
Sylvia Wirth ◽  
Marianna Yanike ◽  
Wendy A. Suzuki ◽  
...  
Keyword(s):  
State Space ◽  
Goodness Of Fit ◽  
State Space Model ◽  
Rate Function ◽  
Spike Rate ◽  
The State ◽  
Space Model ◽  
Wide Range ◽  
Neurophysiological Data ◽  
Rate Functions

The accurate characterization of spike firing rates including the determination of when changes in activity occur is a fundamental issue in the analysis of neurophysiological data. Here we describe a state-space model for estimating the spike rate function that provides a maximum likelihood estimate of the spike rate, model goodness-of-fit assessments, as well as confidence intervals for the spike rate function and any other associated quantities of interest. Using simulated spike data, we first compare the performance of the state-space approach with that of Bayesian adaptive regression splines (BARS) and a simple cubic spline smoothing algorithm. We show that the state-space model is computationally efficient and comparable with other spline approaches. Our results suggest both a theoretically sound and practical approach for estimating spike rate functions that is applicable to a wide range of neurophysiological data.

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.

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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