Published: Apr 6, 2020 by Jhin Woo Choi
A hierarchical spline time series model is being experimented on for forecasting failure rates.
The following properties were observed while configuring the model:
- The sparsity of the data is unbalanced
- Some portions of the data are missing
- Each layer within the model shared similar properties
Sparsity data
Data sparsity could be overcome by estimating B-Spline parameters, \(\beta, w\) for the overall period.
A hierarchical model can be constructed, and its hyperpriors estimated using Markov Chain Monte-Carlo sampling to handle missing data.
This method calculated the basis of B-Spline at equal intervals. It will be possible to build a more reliable model when calculating the basis by quantile division of the number of data according to the amount of data for each section.