scholarly journals A Modeling Study on the Estimation of COVID-19 Daily and Weekly Cases and Reproduction Number Using the Adaptive Kalman Filter: The Example of Ziraat Bank, Turkey

2021 ◽  
pp. 15-27
Author(s):  
İlker Met ◽  
Levent Özbek ◽  
Himmet Aksoy ◽  
Ayfer Erkoç
Keyword(s):  
Time Series ◽  
Kalman Filter ◽  
Reproduction Number ◽  
Jel Classification ◽  
Model Parameters ◽  
Time Varying ◽  
Adaptive Kalman Filter ◽  
Human Race ◽  
Time Varying Parameter ◽  
Case Number

Abstract Since the beginning of 2020, the world has been struggling with a viral epidemic (COVID-19), which poses a serious threat to the collective health of the human race. Mathematical modeling of epidemics is critical for developing such policies, especially during these uncertain times. In this study, the reproduction number and model parameters were predicted using AR(1) (autoregressive time-series model of order 1) and the adaptive Kalman filter (AKF). The data sample used in the study consists of the weekly and daily number of cases amongst the Ziraat Bank personnel between March 11, 2020, and April 19, 2021. This sample was modeled in the state space, and the AKF was used to estimate the number of cases per day. It is quite simple to model the daily and weekly case number time series with the time-varying parameter AR(1) stochastic process and to estimate the time-varying parameter with online AKF. Overall, we found that the weekly case number prediction was more accurate than the daily case number (R2 = 0.97), especially in regions with a low number of cases. We suggest that the simplest method for reproduction number estimation can be obtained by modeling the daily cases using an AR(1) model. JEL classification numbers: C02, C22, C32. Keywords: COVID-19, Modeling, Reproduction number estimation, AR(1), Kalman filter.

Selective Linear Segmentation for Detecting Relevant Parameter Changes*

2020 ◽  
Author(s):  
Arnaud Dufays ◽  
Elysee Aristide Houndetoungan ◽  
Alain Coën
Keyword(s):  
Time Series ◽  
Hedge Fund ◽  
Expectation Maximization Algorithm ◽  
Model Parameters ◽  
Relevant Parameter ◽  
Time Varying ◽  
Long Time ◽  
Model Selection Uncertainty ◽  
Penalized Likelihood Approach ◽  
Changes Over Time

Abstract Change-point (CP) processes are one flexible approach to model long time series. We propose a method to uncover which model parameters truly vary when a CP is detected. Given a set of breakpoints, we use a penalized likelihood approach to select the best set of parameters that changes over time and we prove that the penalty function leads to a consistent selection of the true model. Estimation is carried out via the deterministic annealing expectation-maximization algorithm. Our method accounts for model selection uncertainty and associates a probability to all the possible time-varying parameter specifications. Monte Carlo simulations highlight that the method works well for many time series models including heteroskedastic processes. For a sample of fourteen hedge fund (HF) strategies, using an asset-based style pricing model, we shed light on the promising ability of our method to detect the time-varying dynamics of risk exposures as well as to forecast HF returns.

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).

Online Bayesian Time-varying Parameter Estimation of HIV-1 Time-series*

2012 ◽  
Vol 45 (16) ◽  
pp. 1294-1299 ◽  
Author(s):  
András Hartmann ◽  
Susana Vinga ◽  
João M. Lemos
Keyword(s):  
Time Series ◽  
Parameter Estimation ◽  
Time Varying ◽  
Time Varying Parameter ◽  
Hiv 1

scholarly journals Multi-Fading Factor and Update Monitoring Strategy Adaptive Kalman Filter Based Variational Bayesian with Inaccurate Time-Varying Process and Measure-Ment Noise Covariance Matrices

2020 ◽  
Author(s):  
Chenghao Shan ◽  
Weidong Zhou ◽  
Yefeng Yang ◽  
Zihao Jiang
Keyword(s):  
Kalman Filter ◽  
Covariance Matrix ◽  
Measurement Noise ◽  
Time Varying ◽  
Monitoring Strategy ◽  
Adaptive Kalman Filter ◽  
Variational Bayesian ◽  
Measurement Noise Covariance ◽  
Noise Covariance ◽  
Fading Factor

Aiming at the problem that the performance of Adaptive Kalman filter estimation will be affected when the statistical characteristics of the process and measurement noise matrix are inaccurate and time-varying in the linear Gaussian state-space model, an algorithm of Multi-fading factor and update monitoring strategy adaptive Kalman filter based variational Bayesian is proposed. Inverse Wishart distribution is selected as the measurement noise model, the system state vector and measurement noise covariance matrix are estimated with the variational Bayesian method. The process noise covariance matrix is estimated by the maximum a posteriori principle, and the update monitoring strategy with adjustment factors is used to maintain the positive semi-definite of the updated matrix. The above optimal estimation results are introduced as time-varying parameters into the multiple fading factors to improve the estimation accuracy of the one-step state predicted covariance matrix. The application of the proposed algorithm in target tracking is simulated. The results show that compared with the current filters, the proposed filtering algorithm has better accuracy and convergence performance, and realizes the simultaneous estimation of inaccurate time-varying process and measurement noise covariance matrices.

scholarly journals Structural Breaks dan Ketidakstabilan Permintaan Uang di Indonesia

2017 ◽  
Vol 17 (2) ◽  
pp. 169-183
Author(s):  
Deviyantini Deviyantini ◽  
Iman Sugema ◽  
Tony Irawan
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Interest Rate ◽  
Money Demand ◽  
Structural Breaks ◽  
Demand Function ◽  
Series Data ◽  
Time Varying ◽  
Money Demand Function ◽  
Time Varying Parameter

Structural Breaks and Instability of Money Demand in IndonesiaThis research aims to identify the sources of instability of the money demand function (M1 and M2) due to structural changes that occur as a result of economic shocks. These shocks, are technically shown by the presence of structural breaks in the data and can lead the parameters non-constancy. The instability of the money demand function was analyzed using the Gregory and Hansen test. The source of instability of the money demand was identified using time varying parameter model. This research used quarterly time series data from 1993Q1 to 2013Q4. The result of Gregory and Hansen test indicates there is no long term equilibrium between variables (money demand, income, domestic interest rate, foreign interest rate, exchange rate, and inflation) in the model, neither M1 nor M2 model. On the other word, money demand function is unstable. The source of the instability is exchange rate variable.Keywords: Stability Money Demand; Structural Breaks; Time Varying Parameter ModelAbstrakPenelitian ini bertujuan untuk mengidentifikasi sumber-sumber ketidakstabilan fungsi permintaan uang (M1 dan M2) akibat dari perubahan struktural yang terjadi karena adanya guncangan ekonomi. Guncangan tersebut, yang secara teknis ditunjukkan oleh keberadaan structural breaks di dalam data, dapat menyebabkan parameter menjadi tidak konstan. Ketidakstabilan fungsi permintaan uang dianalisis dengan menggunakan Gregory and Hansen test. Sumber ketidakstabilan dari permintaan uang diidentifikasi dengan menggunakan time varying parameter model. Penelitian ini menggunakan data time series dalam bentuk kuartalan dari 1993Q1 sampai 2013Q4. Hasil Gregory and Hansen test menunjukkan bahwa tidak ada keseimbangan jangka panjang di antara variabel-variabel (permintaan uang, pendapatan, suku bunga domestik, suku bunga luar negeri, nilai tukar, dan inflasi) di dalam model, baik pada model M1 maupun M2. Dengan kata lain, fungsi permintaan uang tidak stabil. Sumber ketidakstabilan tersebut berasal dari variabel nilai tukar.

scholarly journals A Compartmental Epidemic Model Incorporating Probable Cases to Model COVID-19 Outbreak in Regions with Limited Testing Capacity

2020 ◽  
Author(s):  
Agus Hasan ◽  
Yuki Nasution
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Epidemic Model ◽  
Laboratory Testing ◽  
Reproduction Number ◽  
Well Posedness ◽  
Time Varying ◽  
Policy Makers ◽  
Limited Testing ◽  
The Basic Reproduction Number

We propose a new compartmental epidemic model taking into account people who has symptoms with no confirmatory laboratory testing (probable cases). We prove well-posedness of the model and provide an explicit expression for the basic reproduction number R0. We use the model together with an extended Kalman filter (EKF) to estimate the time-varying effective reproduction number Rt of COVID-19 in West Java province, Indonesia, where laboratory testing capacity is limited. Based on our estimation, the value of Rt is higher when the probable cases are taken into account. This correction can be used by decision and policy makers when considering re-opening policy and evaluation of measures.

Sign in / Sign up

Export Citation Format

Share Document