* Check frequency of data
* If sub-daily, use plot_seasonality()
* Format xticks as weekdays for plot_seasonality()
* Display 8 xticks for weekly component
* add first part on build environment and branching
* add rest of workflow tests-pull-requests-docs etc
* Typos and updates
* Highlight Python and R headings
* Reorder sections and condense material
* Few more enhancements
* Issue #1525: Using m.construct_holiday_dataframe() to make sure 'holidays' is a DataFrame.
* #1525 | issuecomment-639399524 | enhancement: have plotly graph the holiday component consistent with matplotlib.
Co-authored-by: Robin Teuwens <teuwens@electris.lu>
* Add R check to travis
* add install step for R
* only run testsrather than R CMD check
* Fix command
* add line for installing dependencies
* fix syntax error
* update docstring with mdape in list for python perf metric func
* re-run python notebook cell to generate mdape in results table
* remove comment
Co-authored-by: Ben Letham <bletham@gmail.com>
To solve this [issue](https://github.com/facebook/prophet/issues/1437) by @chongdae
Korean lunar calendar which is different from Chinese lunar calendar is added python-holidays package recently.
This commit is about dropping the Korean and KR class in hdays.py file and hdays_part1(holidays) will work for Korean holidays.
```
import fbprophet.hdays as hdays_part2
import holidays as hdays_part1
``
* add test and initial function for mdape in R
* Add separate rolling_median_func and tests
* Modify rolling median function
* fix syntax in rolling median function
* sort by h
* R/diagnostics.R
* update .rd docs and notebook
* Add mdape to performance metrics params docstring
* Add test for custom cutoff cv
* implement custom cutoff logic in cv function
* add docstring
* add description in notebook and rebuild .Rd docs
* fix bug and add test case for period is NULL
* replace s.POSIXct set_date
* add implementation for constant trend
* force k and delta params to be 0s
* add tests and fix n_changepoints, changepoints_t to 0
* Add test for cv with constant trend
* Add docs and test for checking invalid input
* make changes to stan
* add transformed params block in stan and output flat trend vector
* correct syntax
* transformed params syntax
* Fix test and port changes to win stan file
* add test for flat trend function
* Add separate function for flat trend init
* fix test
* Add example of dask distributed parameter tuning to notebook diagnostics
* Rearrange description blocks and correct typos
* Add more informative explanations and tips in docs for tuning examples
* increase output df column width to see results
* add instructions for multiprocess and threaded
* reset index of hyperparameter tuning results df
* fix typos
* remove blank code cell
* grammar correction
* Add subsection
* Split different parameter optimsiation examples
I'm not sure why a data frame ends up with names and a tibble does not, but it doesn't seem like an important enough problem to investigate in detail, and this is a simple fix.
+ cleans up Stan model compilation by switching over to the rstantools-based workflow (see https://mc-stan.org/rstantools/articles/minimal-rstan-package.html for more info)
+ minor documentation change: {devtools} -> {remotes}, which is better for end-users
+ adds RStudio project file which makes it easier for community to get started with contributing to the package
* Add functions for serializing to/from JSON
* Fix list vs. series type issue, track version
* Avoid DateTimeIndex
* bugfix
* another fix
* Fix copy test
* Fix issue with pre-epoch dates
* Handle empty datetime series
* modified cross_validation to allow custom cutoffs
* moved set period, initials and identify larg. seas
* modified the diagnostics and added the test
* reverted cv default value tests and added a new custom cutoff test
* reorganized to raise the seasonality period warning message even if cutoffs are manually specified
* moved the initials vs. seasonality check
* changed assertCountEqual to assertItemsEqual in cv
* modified to test lengths instread of cutoff values
Co-authored-by: Fusi Marco <Marco.Fusi@valuelab.it>
* sampling iters arg name, logic changes
* Bump cmdstanpy version in requirements to 0.9.5
* Change Model to CmdStanModel
Co-authored-by: Ben Letham <bletham@gmail.com>
* Add multiprocessing Pool and create function for single cutoff forecast
* add params to single forecast function
* Add iterable for input params for pool
* Add docstring for single cutoff forecast func
* Add check for multiprocessing in test_cross_validation
* check ofr is None and is True and add better description for multiprocess in docstring
* Raise error if wrong args chosen and add test
* fix conflicts
* Change arg to True/False, model.kwargs
* docstring units and few more fixes
* change to iterator and add back tqdm to for loop
* add option in diagnosics notebook about multiprocessing option
* add extra test for checking calls to single forecast func
