trendpy (alpha)

Bayesian trend filtering micro library

I am currently redesigning the API and will release an alpha version in late August.

Documentation

The documentation can be found here.

Contributing

Contribution will be welcomed once a first stable release is ready. [Contact Me]()

Import data

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'])

Requirements

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+)

Sources

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.

Support or Contact

Having trouble with trendpy? Check out our documentation.