I added class comments to each class which explain the high level implementation details. Clamping is supported with natural cubic splines, and this is done by taking the slopes at each endpoint.
Monotonicity is currently not supported (for cubic splines).
".. I had to generate confidence intervals on over 8000 univariate data sets using very small VMs, so I needed to limit large dependancies as much as I could. This package was the result of this!"
Based on the comments in this thread, it may be worth trying to make this package not dependant on Numpy as well?
I did look into stats models, but this was a large library with more than I needed. I did not look into R.. Are there any lightweight alternatives in this language?
That is correct. I had to generate confidence intervals on over 8000 univariate data sets using very small VMs, so I needed to limit large dependancies as much as I could. This package was the result of this!
I authored this package as I needed to generate confidence intervals for time series data without using SciPy. Sharing this here, as this could be a useful package for others :)
Included Models:
- Linear regression
- Ridge regression
- Linear spline
- Isotonic regression
- Bin regression
- Cubic spline
- Natural cubic spline
- Exponential moving average
- Kernel functions (Gaussian, KNN, Weighted average)
Monotonicity is currently not supported (for cubic splines).