v0.5.0

Helpers for the steps around a forecast: list the dates to forecast, check how well a model fits,
score forecasts by month or with your own metrics, and try every combination of settings. Existing
code works as before.

Added

• makeFutureTimestamps lists the dates to forecast after a history or a timestamp, like
  Python's make_future_dataframe. The history's own dates come first unless you pass
  includeHistory: false. It takes pandas' h, D, W, W-MON to W-SAT, MS and ME, and
  matches pandas on month ends, leap years and time of day. See
  Future rows.
• residuals predicts each measurement with a fitted model and returns actual, predicted
  and residual per row, ready to chart. It never refits. See
  Checking the fit.
• Monthly horizons. performanceMetrics and comparePerformance take
  horizon: "calendar-month" to group forecasts by calendar months from their cutoff, like
  Python's performance_metrics(monthly=True). Scores then carry horizonMonths instead of
  horizonMs. See
  Grouping by calendar month.
• Custom metrics, like Python's register_performance_metric. Pass your own definition next to
  the built-in names in performanceMetrics or comparePerformance, or as the objective of
  searchModels. See
  Your own metrics.
• makeParameterGrid expands base options and lists of values into every combination, as
  candidates for searchModels, and checks each one before the search starts. See
  Trying every combination.
• seasonalityPriorScale and holidaysPriorScale, like Python's seasonality_prior_scale and
  holidays_prior_scale. Both default to 10 and set the prior for every seasonality, or every
  event, country holiday and regressor, that doesn't set its own priorScale. As in Python,
  regressors follow holidaysPriorScale. See
  Choosing settings.
• maximumSearchCandidates (32) is exported.

Changed

• rows, horizons and rolling metric reports include a horizon field, "duration" or
  "calendar-month", saying which unit their horizons use.
• Error fields accept new values: InputValidationError.input can be "parameter-grid", and
  EvaluationMetricError.metric can be a custom metric's name, with a new "zero-denominator"
  reason and score, quantile and finalize stages. Update exhaustive checks on these fields.
• parseEventCalendar, parseRegressorDefinitions and parseCountryHolidays take an optional
  default prior scale for entries without their own. Called alone, they still default to 10.

Fixed

• The fit span for a flat trend now counts one parameter per changepoint in parameter.count,
  matching what the engine solves for. changepoint.count stays 0, because a flat trend never
  bends.

Verification against Python Prophet

• New fixtures check makeFutureTimestamps against make_future_dataframe, monthly and custom
  metrics against performance_metrics, and the global prior scales against Python's constructor
  options.
• New benchmarks score cross-validation by calendar month and with custom metrics against the
  same work in Python, on identical forecast rows, and rank a model search by a custom metric.
• New benchmarks fit country holidays, events, a seasonality and a regressor with small global
  prior scales, with and without per-component overrides.

Full Changelog: v0.4.0...v0.5.0