Add extrapolate_season.predict() function

- 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

tests/config.py

@@ -16,10 +16,15 @@ NOON = 12
 START = datetime.datetime(YEAR, MONTH, DAY, config.SERVICE_START, 0)
 END = datetime.datetime(YEAR, MONTH, 15, config.SERVICE_END, 0)
 
-# Default time steps, for example, for `OrderHistory` objects.
+# Default time steps (in minutes), for example, for `OrderHistory` objects.
 LONG_TIME_STEP = 60
 SHORT_TIME_STEP = 30
 TIME_STEPS = (SHORT_TIME_STEP, LONG_TIME_STEP)
+# The `frequency` of vertical time series is the number of days in a week, 7,
+# times the number of time steps per day. With 12 operating hours (11 am - 11 pm)
+# the `frequency`s are 84 and 168 for the `LONG/SHORT_TIME_STEP`s.
+VERTICAL_FREQUENCY_LONG = 7 * 12
+VERTICAL_FREQUENCY_SHORT = 7 * 24
 
 # Default training horizons, for example, for
 # `OrderHistory.make_horizontal_time_series()`.