alex-hse-repository

from etna.analysis import plot_imputation
from etna.transforms import TimeSeriesImputerTransform

ts = get_ts(["All_American_Ensign"])
imputer = TimeSeriesImputerTransform(in_column="target", strategy="zero")
plot_imputation(ts, imputer, start="2016-04-01", end="2017-05-01") 

alex-hse-repository

imputer = TimeSeriesImputerTransform(in_column="target", strategy="forward_fill")
plot_imputation(ts, imputer, start="2016-04-01", end="2017-05-01")

alex-hse-repository

imputer = TimeSeriesImputerTransform(in_column="target", strategy="running_mean", window=3)
plot_imputation(ts, imputer, start="2016-04-01", end="2017-05-01")

alex-hse-repository

from etna.analysis import plot_anomalies, get_anomalies_density

ts = get_ts(["East_Midlands"])
anomalies = get_anomalies_density(ts, window_size=30, distance_coef=1, n_neighbors=9)
plot_anomalies(ts, anomalies)

alex-hse-repository

from etna.pipeline import Pipeline
from etna.models import ProphetModel
from etna.transforms import DensityOutliersTransform
from etna.metrics import SMAPE

HORIZON = 62
pipeline = Pipeline(model=ProphetModel(), transforms=[], horizon=HORIZON)
pipeline_outliers = Pipeline(
    model=ProphetModel(),
    transforms=[

alex-hse-repository

ts = get_ts(["Black_Sails"])
ts.fit_transform([TimeSeriesImputerTransform(in_column="target", strategy="running_mean", window=3)])
ts.plot()

alex-hse-repository

from etna.analysis import plot_holidays

holidays_df = pd.DataFrame({
  'holiday': 'season',
  'ds': pd.to_datetime(["2016-01-23", "2017-01-29"]),
  'lower_window': -21,
  'upper_window': 90+14,
})
plot_holidays(ts, holidays=holidays_df)

alex-hse-repository

from etna.analysis import plot_backtest

HORIZON = 365
pipeline = Pipeline(model=ProphetModel(), transforms=[], horizon=HORIZON)
metrics, forecasts, _ = pipeline.backtest(ts, metrics=[SMAPE()], n_folds=1)
plot_backtest(forecast, ts)

alex-hse-repository

pipeline = Pipeline(
  model=ProphetModel(yearly_seasonality=True),
  transforms=[],
  horizon=HORIZON
)
metrics, forecast, _ = pipeline.backtest(ts, metrics=[SMAPE()], n_folds=1)
plot_backtest(forecast, ts)

alex-hse-repository

pipeline = Pipeline(
    model=ProphetModel(yearly_seasonality=True, holidays=holidays_df),
    transforms=[],
    horizon=HORIZON
)
metrics, forecast, _ = pipeline.backtest(ts, metrics=[SMAPE()], n_folds=1)
plot_backtest(forecast, ts)