Recursive Least Square: RLS Method-Based Time Series Data Prediction for Many Missing Data

Prediction methods for time series data with many missing data based on Recursive Least Square (RLS) method are proposed. There are two parameter tuning algorithms, time update and measurement update algorithms for parameter estimation of Kalman filter. Two learning methods for parameter estimation of Kalman filter are proposed based on RLS method. One is the method without measurement update algorithm (RLS-1). The other one is the method without both time and measurement update algorithms (RLS-2). The methods are applied to the time series data of Defense Meteorological Satellite Program (DMSP) / Special Sensor Microwave/Imager (SSM/I) data with a plenty of missing data. It is found that the proposed RLS-2 method shows smooth and fast convergence in learning process in comparison to the RLS-1.

@article{Arai2020,
  title     = {Recursive Least Square: RLS Method-Based Time Series Data Prediction for Many Missing Data},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {11},
  number    = {11},
  year      = {2020},
  publisher = {The Science and Information Organization},
  author    = {Kohei Arai and Kaname Seto},
  doi       = {10.14569/IJACSA.2020.0111109},
  url       = {https://doi.org/10.14569/IJACSA.2020.0111109}
}