I want to filter and reduce variation in time series data. Please suggestion me some good alternative techniques of Kalman filter that used for reduce noise and variation in the time series data.
Kalman filter is a tool for using weighted error as a corrective mechanism for predictive value. In the old days, it was commonly used for the development homing guidance in missile launching and control. Thus, the development in this field saw further development in control theory and servomechanism, see Poalumbo et al. Article link below. Development in this field could also be used or applied in social science because homing guidance theory is quite developed for dealing with noise and variation. Whether applyjng it in missile guidace or developing econometric modelling, it is a matter of system control and analysis; thus, error or noise correction method are keyed. The two articles linked below may provide a refreshing approach via cross-disciplinary method. Do take a look, it may be worth your while. Cheers.
LINKS:
https://www.google.com/url?sa=t&source=web&rct=j&url=http://www.dtic.mil/dtic/tr/fulltext/u2/294534.pdf&ved=2ahUKEwiblqWJytLeAhXLsI8KHe0XArgQFjACegQICBAB&usg=AOvVaw16riFmn3Pq_2u9Bzcpzr46
https://www.google.com/url?sa=t&source=web&rct=j&url=https://www.jhuapl.edu/techdigest/TD/td2901/Palumbo_Homing.pdf&ved=2ahUKEwiblqWJytLeAhXLsI8KHe0XArgQFjADegQIBhAB&usg=AOvVaw16NUjK4WOkYLS2QHesGgdY