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Fix/backtest multiple series #1517

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merged 2 commits into from
Jan 26, 2023
Merged

Fix/backtest multiple series #1517

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6363099
add a unit test capturing a bug
hrzn Jan 26, 2023
9a383f3
fix a bug in backtesting with multiple series
hrzn Jan 26, 2023
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5 changes: 2 additions & 3 deletions darts/models/forecasting/forecasting_model.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1052,11 +1052,10 @@ def backtest(
errors = errors[0]
backtest_list.append(errors)
else:

errors = [
[metric_f(series, f) for metric_f in metric]
[metric_f(target_ts, f) for metric_f in metric]
if len(metric) > 1
else metric[0](series, f)
else metric[0](target_ts, f)
for f in forecasts[idx]
]

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29 changes: 28 additions & 1 deletion darts/tests/models/forecasting/test_backtesting.py
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Original file line number Diff line number Diff line change
Expand Up @@ -7,9 +7,17 @@
import pytest

from darts import TimeSeries
from darts.datasets import AirPassengersDataset, MonthlyMilkDataset
from darts.logging import get_logger
from darts.metrics import mape, r2_score
from darts.models import ARIMA, FFT, ExponentialSmoothing, NaiveDrift, Theta
from darts.models import (
ARIMA,
FFT,
ExponentialSmoothing,
NaiveDrift,
NaiveSeasonal,
Theta,
)
from darts.tests.base_test_class import DartsBaseTestClass
from darts.utils.timeseries_generation import gaussian_timeseries as gt
from darts.utils.timeseries_generation import linear_timeseries as lt
Expand Down Expand Up @@ -257,6 +265,25 @@ def test_backtest_forecasting(self):
self.assertEqual(pred.width, 2)
self.assertEqual(pred.end_time(), linear_series.end_time())

def test_backtest_multiple_series(self):
series = [AirPassengersDataset().load(), MonthlyMilkDataset().load()]
model = NaiveSeasonal(K=1)

error = model.backtest(
series,
train_length=30,
forecast_horizon=2,
stride=1,
retrain=True,
last_points_only=False,
verbose=False,
)

expected = [11.63104, 6.09458]
self.assertEqual(len(error), 2)
self.assertAlmostEqual(error[0], expected[0], places=4)
self.assertAlmostEqual(error[1], expected[1], places=4)

@unittest.skipUnless(TORCH_AVAILABLE, "requires torch")
def test_backtest_regression(self):
np.random.seed(4)
Expand Down