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/forecasts/timify/conftest.py

@@ -47,7 +47,10 @@ def order_totals(good_pixel_id):
 
 @pytest.fixture
 def order_history(order_totals, grid):
-    """An `OrderHistory` object that does not need the database."""
+    """An `OrderHistory` object that does not need the database.
+
+    Uses the LONG_TIME_STEP as the length of a time step.
+    """
     oh = timify.OrderHistory(grid=grid, time_step=test_config.LONG_TIME_STEP)
     oh._data = order_totals