- the model applies a simple moving average on horizontal time series
- refactor `db.Forecast.from_dataframe()` to correctly convert
`float('NaN')` values into `None`; otherwise, SQLAlchemy complains
- the method implements a heuristic from the first research paper
that chooses the most promising forecasting `*Model` based on
the average daily demand in a `Pixel` for a given `train_horizon`
- adjust the test scenario => `LONG_TRAIN_HORIZON` becomes `8`
as that is part of the rule implemented in the heuristic
- `*Model`s use the `methods.*.predict()` functions to predict demand
given an order time series generated by `timify.OrderHistory`
- `models.base.ForecastingModelABC` unifies how all `*Model`s work
and implements a caching strategy
- implement three `*Model`s for tactical forecasting, based on the
hets, varima, and rtarima models described in the first research paper
- add overall documentation for `urban_meal_delivery.forecasts` package
- move the fixtures in `tests.forecasts.timify.conftest` to
`tests.forecasts.conftest` and adjust the horizon of the test horizon
from two to three weeks
- the function implements a forecasting "method" similar to the
seasonal naive method
=> instead of simply taking the last observation given a seasonal lag,
it linearly extrapolates all observations of the same seasonal lag
from the past into the future; conceptually, it is like the
seasonal naive method with built-in smoothing
- the function is tested just like the `arima.predict()` and
`ets.predict()` functions
+ rename the `tests.forecasts.methods.test_ts_methods` module
into `tests.forecasts.methods.test_predictions`
- re-organize some constants in the `tests` package
- streamline some docstrings
- move the module
- unify the corresponding tests in `tests.forecasts.methods` sub-package
- make all `predict()` and the `stl()` function round results
- streamline documentation
- ensure a `Restaurant` only has one unique `Order.pickup_address`
- rework `Grid.gridify()` so that only pickup addresses are assigned
into `Pixel`s
- include database migrations to ensure the data adhere to these
tighter constraints
- add `.low80`, `.high80`, `.low95`, and `.high95` columns
- add check contraints for the confidence intervals
- rename the `.method` column into `.model` for consistency
- the main purpose of this class is to manage querying the order totals
from the database and slice various kinds of time series out of the
data
- the class holds the former `aggregate_orders()` function as a method
- modularize the corresponding tests
- add `tests.config` with globals used when testing to provide a
single source of truth for various settings
- the function queries the database and aggregates the ad-hoc orders
by pixel and time steps into a demand time series
- implement "heavy" integration tests for `aggregate_orders()`
- make `pandas` a package dependency
- streamline the `Config`
