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CHANGELOG.md

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Changelog

Upates in 0.1.1.10

  • Add an auto noise initializer which initializes the model noise according to the scale of the time series. It is proved to improve the model performance over small scale data. To use auto initializer, simply call dlm.noisePrior() after constructing the model.
mydlm = dlm(data) + trend(1)
mydlm.noisePrior()
mydlm.fit()
  • Update the default variance of the components to be 100.
  • Fixed map() delayed evaluation bug for python3 (Thanks @bdewilde!).
  • Fixed bug in continuePrediction() for autoReg component. Now predictN() can work with autoReg (Thanks @Usman!).
  • Fixed a bug in predictN() which modifies the status of the dlm object. Now dlm.predictN() can be followed by dlm.append() and dlm.fit(). (Thanks @albertotb)

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 from dlm.predictN() on top of the time series data.
  • Add dlm.predictN() which allows prediction over multiple days.
  • Change the degree of trend to match the actual meaning in polynomial, i.e, degree=0 stands for constant and degree=1 stands for linear trend and so on so forth.
  • Add support for missing data in modelTuner and .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