scholarly journals PERAMALAN JUMLAH KUNJUNGAN WISATAWAN AUSTRALIA YANG BERKUNJUNG KE BALI MENGGUNAKAN MODEL TIME VARYING PARAMETER (TVP)

2016 ◽  
Vol 5 (3) ◽  
pp. 117
Author(s):  
I PUTU GEDE DIAN GERRY SUWEDAYANA ◽  
I WAYAN SUMARJAYA ◽  
NI LUH PUTU SUCIPTAWATI
Keyword(s):  
Kalman Filter ◽  
State Space Model ◽  
Percentage Error ◽  
Time Varying ◽  
Mean Square ◽  
Explanatory Variables ◽  
Space Model ◽  
A Value ◽  
Time Varying Parameter ◽  
Kalman Filter Algorithm

The purpose of this research is to forecast the number of Australian tourists arrival to Bali using Time Varying Parameter (TVP) model based on inflation of Indonesia and exchange rate AUD to IDR from January 2010 – December 2015 as explanatory variables. TVP model is specified in a state space model and estimated by Kalman filter algorithm. The result shows that the TVP model can be used to forecast the number of Australian tourists arrival to Bali because it satisfied the assumption that the residuals are distributed normally and the residuals in the measurement and transition equations are not correlated. The estimated TVP model is . This model has a value of mean absolute percentage error (MAPE) is equal to dan root mean square percentage error (RMSPE) is equal to . The number of Australian tourists arrival to Bali for the next five periods is predicted: ; ; ; ; and (January - May 2016).

Analysis of Operating Deflection Shapes Based on Subspace Method

1997 ◽  
Author(s):  
Nobutaka Tsujiuchi ◽  
Yuichi Matsumura ◽  
Takayuki Koizumi
Keyword(s):  
Experimental Data ◽  
Time Domain ◽  
State Space Model ◽  
Measurement Data ◽  
Time Varying ◽  
Subspace Method ◽  
Identification Scheme ◽  
Space Model ◽  
Time Varying Systems ◽  
Frequency Components

Abstract In this paper, we propose the new method to identify the Operating Deflection Shapes (ODSs) from the measurement data of time domain. At first, we present the identification scheme of ODSs based on a state-space model. Then the scheme is extended to identify the ODSs adaptively for the time-varying systems by using the URV Decomposition (URVD). Proposed scheme is able to decompose the deformation of a structure under operating condition into the underlying superposition of well excited frequency components. This paper introduces the algorithm and shows the effectiveness of our proposed scheme applyed for both synthesized and experimental data.

scholarly journals Variational Bayesian-Based Improved Maximum Mixture Correntropy Kalman Filter for Non-Gaussian Noise

Entropy ◽  
2022 ◽  
Vol 24 (1) ◽  
pp. 117
Author(s):  
Xuyou Li ◽  
Yanda Guo ◽  
Qingwen Meng
Keyword(s):  
Kalman Filter ◽  
Gaussian Noise ◽  
State Space Model ◽  
Performance Evaluations ◽  
Gaussian State ◽  
Variational Bayesian ◽  
Space Model ◽  
Bandwidth Parameter ◽  
Non Gaussian ◽  
Filtering Problem

The maximum correntropy Kalman filter (MCKF) is an effective algorithm that was proposed to solve the non-Gaussian filtering problem for linear systems. Compared with the original Kalman filter (KF), the MCKF is a sub-optimal filter with Gaussian correntropy objective function, which has been demonstrated to have excellent robustness to non-Gaussian noise. However, the performance of MCKF is affected by its kernel bandwidth parameter, and a constant kernel bandwidth may lead to severe accuracy degradation in non-stationary noises. In order to solve this problem, the mixture correntropy method is further explored in this work, and an improved maximum mixture correntropy KF (IMMCKF) is proposed. By derivation, the random variables that obey Beta-Bernoulli distribution are taken as intermediate parameters, and a new hierarchical Gaussian state-space model was established. Finally, the unknown mixing probability and state estimation vector at each moment are inferred via a variational Bayesian approach, which provides an effective solution to improve the applicability of MCKFs in non-stationary noises. Performance evaluations demonstrate that the proposed filter significantly improves the existing MCKFs in non-stationary noises.

Estimating Time-Varying Networks With a State-Space Model

2020 ◽  
Author(s):  
Shaowen Liu ◽  
Massimiliano Caporin ◽  
Sandra Paterlini
Keyword(s):  
State Space ◽  
State Space Model ◽  
Time Varying ◽  
Space Model ◽  
Time Varying Networks

scholarly journals A Hybrid Nonlinear Forecasting Strategy for Short-Term Wind Speed

Energies ◽  
2020 ◽  
Vol 13 (7) ◽  
pp. 1596 ◽  
Author(s):  
Xin Zhao ◽  
Haikun Wei ◽  
Chenxi Li ◽  
Kanjian Zhang
Keyword(s):  
Kalman Filter ◽  
Wind Speed ◽  
State Space ◽  
State Space Model ◽  
Hybrid Approach ◽  
Wind Farms ◽  
Short Term ◽  
Forecasting Accuracy ◽  
Space Model ◽  
Wind Speeds

The ability to predict wind speeds is very important for the security and stability of wind farms and power system operations. Wind speeds typically vary slowly over time, which makes them difficult to forecast. In this study, a hybrid nonlinear estimation approach combining Gaussian process (GP) and unscented Kalman filter (UKF) is proposed to predict dynamic changes of wind speed and improve forecasting accuracy. The proposed approach can provide both point and interval predictions for wind speed. Firstly, the GP method is established as the nonlinear transition function of a state space model, and the covariance obtained from the GP predictive model is used as the process noise. Secondly, UKF is used to solve the state space model and update the initial prediction of short-term wind speed. The proposed hybrid approach can adjust dynamically in conjunction with the distribution changes. In order to evaluate the performance of the proposed hybrid approach, the persistence model, GP model, autoregressive (AR) model, and AR integrated with Kalman filter (KF) model are used to predict the results for comparison. Taking two wind farms in China and the National Renewable Energy Laboratory (NREL) database as the experimental data, the results show that the proposed hybrid approach is suitable for wind speed predictions, and that it can increase forecasting accuracy.

ASSESSING PRE-CRISIS FUNDAMENTALS IN SELECTED ASIAN STOCK MARKETS

2005 ◽  
Vol 50 (02) ◽  
pp. 175-196 ◽  
Author(s):  
EE LENG LAU ◽  
G. K. RANDOLPH TAN ◽  
SHAHIDUR RAHMAN
Keyword(s):  
Kalman Filter ◽  
Emerging Markets ◽  
State Space ◽  
Stock Prices ◽  
Stock Markets ◽  
Bubble Formation ◽  
State Space Model ◽  
Space Model ◽  
Rational Bubbles ◽  
Space Formulation

In the folklore of emerging markets, there is a popular belief that bubbles are inevitable. In this paper, our objective is to estimate a state-space model for rational bubbles in selected Asian economies with the aid of the Kalman Filter. For each economy, we derive a possible picture of the bubble formation process that is implied by the state-space formulation. The estimation is based on the rational valuation formula for stock prices. Our results provide a possible way of defining the presence of rational bubbles in the stock markets of Taiwan, Singapore, Korea, and Malaysia.

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