Tomonori Masui tomonori-masui

tomonori-masui / nile_autoarima.py

Last active July 4, 2021 20:55

	test_len = int(len(ts_nl) * 0.3)
	nl_train, nl_test = ts_nl.iloc[:-test_len], ts_nl.iloc[-test_len:]

	forecaster = AutoARIMA(suppress_warnings=True)
	forecaster.fit(nl_train)
	forecaster.summary()

tomonori-masui / nile_autoarima_plot

Last active March 10, 2023 06:39

	fh = np.arange(test_len) + 1
	forecast = forecaster.predict(fh=fh)
	forecast_int = forecaster.predict_interval(fh=fh, coverage=coverage)['Coverage'][coverage]
	nl_arima_mae, nl_arima_mape = plot_forecast(nl_train, nl_test, forecast, forecast_int)

tomonori-masui / nile_lgbm.py

Created July 2, 2021 05:19

	param_grid = {"window_length": [5, 10, 15, 20, 25, 30]}
	forecaster = create_forecaster()
	nl_lgb_mae, nl_lgb_mape = grid_serch_forecaster(nl_train, nl_test, forecaster, param_grid)

tomonori-masui / wpi.py

Created July 3, 2021 17:59

	import requests
	from io import BytesIO

	wpi1 = requests.get("https://www.stata-press.com/data/r12/wpi1.dta").content
	data = pd.read_stata(BytesIO(wpi1))
	ts_wpi = data.set_index("t").wpi

	tsplot(ts_wpi)

tomonori-masui / wpi_first_diff.py

Created July 3, 2021 18:02

	ts_wpi_diff = (ts_wpi - ts_wpi.shift(1)).dropna()
	tsplot(ts_wpi_diff)

tomonori-masui / wpi_2nd_diff.py

Created July 3, 2021 18:07

	ts_wpi_2diff = (ts_wpi_diff - ts_wpi_diff.shift(1)).dropna()
	tsplot(ts_wpi_2diff)

tomonori-masui / wpi_arima.py

Created July 3, 2021 18:11

	ts_wpi.index = ts_wpi.index.to_period("Q")
	test_len = int(len(ts_wpi) * 0.25)
	wpi_train, wpi_test = ts_wpi.iloc[:-test_len], ts_wpi.iloc[-test_len:]

	forecaster = AutoARIMA(suppress_warnings=True)
	forecaster.fit(wpi_train)
	forecaster.summary()

tomonori-masui / wpi_arima_eval.py

Last active March 10, 2023 06:39

	fh = np.arange(test_len) + 1
	forecast = forecaster.predict(fh=fh)
	forecast_int = forecaster.predict_interval(fh=fh, coverage=coverage)['Coverage'][coverage]
	wpi_arima_mae, wpi_arima_mape = plot_forecast(
	wpi_train, wpi_test, forecast, forecast_int
	)

tomonori-masui / wpi_lgbm.py

Created July 3, 2021 18:22

	param_grid = {"window_length": [5, 10, 15, 20, 25, 30]}
	forecaster = create_forecaster()
	wpi_lgb_mae, wpi_lgb_mape = grid_serch_forecaster(
	wpi_train, wpi_test, forecaster, param_grid
	)

tomonori-masui / wpi_detrending.py

Last active July 3, 2021 18:30

	from sktime.forecasting.trend import PolynomialTrendForecaster
	from sktime.transformations.series.detrend import Detrender

	# linear detrending
	forecaster = PolynomialTrendForecaster(degree=1)
	transformer = Detrender(forecaster=forecaster)
	yt = transformer.fit_transform(wpi_train)

	forecaster = PolynomialTrendForecaster(degree=1)
	fh_ins = -np.arange(len(wpi_train)) # in-sample forecasting horizon