Updates in 0.1.1.9
- Add an example from Google data science blog and updated the homepage
- Add
dlm.plotPredictN()which plots the prediction result fromdlm.predictN()on top of the time series data. - Add
dlm.predictN()which allows prediction over multiple days. - Change the
degreeoftrendto match the actual meaning in polynomial, i.e,degree=0stands for constant anddegree=1stands for linear trend and so on so forth. - Add support for missing data in
modelTunerand.getMSE()(Thanks @sun137653577)
Updates in 0.1.1.8
- Add an modelTuner class to auto-tune the discounting factors using gradient descent.
- Add model evaluation methods for geting residuals and MSE (one-day a head predicted loss).
- Bug fix: Fix the incorrect return length of the DLM results.
- Add travis Build test and coverage test (Thanks @liguopku).
- Modify the tests on all Numpy matrix to pass Python3 tests.
- dynamic component now accepts Numpy Matrix as feature input (Thanks @xgdgsc).
- Update the doc to be more human readable (Thanks @xgdgsc).
Updates in 0.1.1.7 (Special Thanks to Dr. Nick Gayeski for helping identify all these issues!)
- Add an option to let different component evolve independently (default)
- Bug fixing: change the default prior covariance for the components to match the results of BATS
- Bug fixing: deprecate the hand-written generalized inverse function and switch to numpy's built-in one.
- Add an easy specification for component prior on covariance and the model prior on observational noise (see the example and the user manual)
updates in 0.1.1.1
- Fix bugs in latent states retrieval
- Rewrite all the get methods (simpler and concise). Allows easy fetching individual component.
- Add a longSeason component
- Add more plot functionalities
- Add the ribbon confidence interval
- Add a simple example in documentation for using pydlm