Online Motion-Artifact Removal in fNIRS Signals: Combined Square-Root Cubature Kalman Filter and Weighted Moving Average Model Approach

2020 ◽  
Author(s):  
Ruisen Huang ◽  
Dalin Yang ◽  
Kunqiang Qing ◽  
Keum-Shik Hong
Keyword(s):  
Kalman Filter ◽  
Moving Average ◽  
Motion Artifact ◽  
Square Root ◽  
Average Model ◽  
Artifact Removal ◽  
Cubature Kalman Filter ◽  
Moving Average Model ◽  
Weighted Moving Average ◽  
Model Approach

A Linear Poisson Autoregressive Model: The Poisson AR(p) Model

2001 ◽  
Vol 9 (2) ◽  
pp. 164-184 ◽  
Author(s):  
Patrick T. Brandt ◽  
John T. Williams
Keyword(s):  
Time Series ◽  
Political Science ◽  
Autoregressive Model ◽  
Alternative Model ◽  
Moving Average ◽  
Dynamic Processes ◽  
Average Model ◽  
Moving Average Model ◽  
Poisson Autoregressive Model ◽  
Weighted Moving Average

Time series of event counts are common in political science and other social science applications. Presently, there are few satisfactory methods for identifying the dynamics in such data and accounting for the dynamic processes in event counts regression. We address this issue by building on earlier work for persistent event counts in the Poisson exponentially weighted moving-average model (PEWMA) of Brandt et al. (American Journal of Political Science44(4):823–843, 2000). We develop an alternative model for stationary mean reverting data, the Poisson autoregressive model of orderp, or PAR(p) model. Issues of identification and model selection are also considered. We then evaluate the properties of this model and present both Monte Carlo evidence and applications to illustrate.

scholarly journals Transient Weighted Moving-Average Model of Photovoltaic Module Back-Surface Temperature

2020 ◽  
Vol 10 (4) ◽  
pp. 1053-1060
Author(s):  
Matthew Prilliman ◽  
Joshua S. Stein ◽  
Daniel Riley ◽  
Govindasamy Tamizhmani
Keyword(s):  
Surface Temperature ◽  
Moving Average ◽  
Back Surface ◽  
Photovoltaic Module ◽  
Average Model ◽  
Moving Average Model ◽  
Weighted Moving Average

scholarly journals Forecasting Needs Of Mountain Types Of DDD Bike Using The Seasonal Autoregressive Integrated Moving Average Model Approach

2021 ◽  
Vol 1 (2) ◽  
Author(s):  
Didin Muhjidin ◽  
Tedjo Sukmono
Keyword(s):  
Moving Average ◽  
Market Demand ◽  
Average Model ◽  
Sarima Model ◽  
Autoregressive Integrated Moving Average ◽  
Seasonal Time Series ◽  
Moving Average Model ◽  
Make To Stock ◽  
Market Needs ◽  
Model Approach

One of the bicycle manufacturers in Indonesia, namely PT. DDD is a manufacture engaged in the production of various types of bicycles with a make to stock production system. Market demand that fluctuates every year results in a lack of readiness to meet market needs. So a re-planning is needed in order to meet all market demands. The Box Jenkins statistical method, the Seasonal Autoregressive Integrated Moving Average model, is one of the appropriate approaches to solve problems at PT. DDD. The advantages of the SARIMA model can be used to forecast seasonal or non-seasonal time series simultaneously. The best SARIMA model approach to forecasting demand for mountain bikes at PT. DDD is SARIMA (0,0,0)(0,1,1)12 with the equation Zt=Zt-12+ΘQat-12+at with the smallest MAPE value of 32.35%. So that the model is said to be feasible to predict mountain bikes and the model can predict up to 12 periods in 2021.

Square-Root Cubature Kalman Filter and Its Application to SLAM of an Mobile Robot

ROBOT ◽  
2013 ◽  
Vol 35 (2) ◽  
pp. 186 ◽  
Author(s):  
Yifei KANG ◽  
Yongduan SONG ◽  
Yu SONG ◽  
Deli YAN ◽  
Danyong LI
Keyword(s):  
Kalman Filter ◽  
Mobile Robot ◽  
Square Root ◽  
Cubature Kalman Filter
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