Bayesian trend filtering micro library
I am currently redesigning the API and will release an alpha version in late August.
The documentation can be found here.
Contribution will be welcomed once a first stable release is ready. Contact Me
Data is imported from a file (trendpy only supports csv for now).
# import data from csv file (with dates and price) -- for now trendpy only
# support 1D time series
from trendpy import filter
from pandas import read_csv
filename='data.csv'
data = read_csv(filename)
filtered = filter(data['time series'])These requirements reflect the testing environment. It is possible that trendpy will work with older versions.
- Python (3+)
- NumPy (1.12+)
- SciPy (0.13+)
- Pandas (0.19+)
- matplotlib (2.0+)
- statsmodels (0.6+)
Research papers that helped develop this library
- Locally adaptative regression splines (1997) - Mammen, van der Geer
- Asymptotic equivalence of non-parametric regression and white noise (1996) - Brown, Lo
- Postwar US business cycles: an empirical investigation (1997) - Hodrick Prescott
- Regression Shrinkage and Selection via the Lasso - (1996) Tibshirani
- Lasso Regression: Estimation and Shrinkage via Limit of Gibbs Sampling - (2015) Rayaratnam et al.
- Assessing Convergence of the Markov Chain Monte Carlo Method in Multivariate Case - (2005) Nogueira et al.
Having trouble with trendpy? Check out our documentation.