What’s new in Pyramid¶
As new releases of Pyramid are pushed out, the following list (introduced in v0.8.1) will document the latest features.
- Explicitly catch case in
auto_arimawhere a value of
mthat is too large may over-estimate
D, causing the time series to be differenced down to an empty array. This is now handled by raising a separate error for this case that better explains what happened.
- Re-pickling an
ARIMAwill no longer remove the location on disk of the cached
statsmodelsARIMA models. Older versions encountered an issue where an older version of the model would be reinstated and immediately fail due to an OSError since the cached state no longer existed. This means that a user must be very intentional about clearing out the pyramid cache over time.
- Added pyramid cache check on initial import to warn user if the cache size has grown too large.
Dare explicitly defined for
None), do not raise an error if they exceed
- Added Circle CI for validating PyPy builds (rather than CPython)
- Deploy python wheel for version 3.6 on Linux and Windows
- Include warning for upcoming package name change (
pkg_version_attribute (assigned on model
fit) is new as of version 0.8.0. On unpickling, if the current Pyramid version does not match the version under which it was serialized, a
UserWarningwill be raised.
Addition of the
_config.pyfile at the top-level of the package
- Specifies the location of the ARIMA result pickles (see Serializing your ARIMA models)
- Specifies the ARIMA result pickle name pattern
Fix bug (Issue #30) in
ARIMAwhere using CV with differencing and no seasonality caused a dim mismatch in the model’s exog array and its endog array
New dataset: Woolyrnq (from R’s
Visualization utilities available at the top level of the package:
Updated documentation with significantly more examples and API references.
- In prior versions, the
out_of_sample_size(OOSS) parameter misbehaved in the sense that it ended up fitting the model on the entire sample, and scoring the number specified. This behavior changed in v0.7.0. Going forward, when OOSS is not None, ARIMA models will be fit on \(n - OOSS\) samples, scored on the last OOSS samples, and the held-out samples are then added to the model.
- In prior versions, the
- This method adds new samples to the model, effectively refreshing the point from which it creates new forecasts without impacting the model parameters.
ARIMAconfidence intervals on
return_conf_intis true, the confidence intervals will now be returned with the forecasts.
- No longer compute the \(U\) or \(V\) matrix in the SVD computation in the Canova-Hansen test. This makes the test much faster.