Troubleshooting Common Errors

Troubleshooting Common Errors

When to Use

Use this skill when generated skforecast code raises an error, produces unexpected results, or uses an import path, class name or function that no longer exists. It is the full pitfall catalog; the highest-risk skills also carry a focused ## Stop Conditions table that points back here.

Related skills

• Alongside: every skill — this is the canonical pitfall catalog their ## Stop Conditions tables point back to
• Related: complete-api-reference (confirm the exact signature before assuming a bug)
• Related: choosing-a-forecaster (many errors come from using the wrong forecaster for the data shape)

Deprecated Import Paths

The most frequent LLM error. Old import paths no longer exist.

Wrong Class/Function Names

Removed Arguments

Categorical Exogenous Variables

# ❌ WRONG: setting categorical features directly on the estimator
forecaster = ForecasterRecursive(
    estimator=LGBMRegressor(categorical_feature=[0, 1]),
    lags=24,
)

# ✅ CORRECT: use categorical_features parameter on the forecaster
forecaster = ForecasterRecursive(
    estimator=LGBMRegressor(),
    lags=24,
    categorical_features='auto',  # or ['col_name_1', 'col_name_2']
)

Data Issues

"ValueError: The index of the series must be a DatetimeIndex with frequency"

# Fix: set the frequency
data = data.asfreq('h')       # Hourly
data = data.asfreq('D')       # Daily
data = data.asfreq('MS')      # Monthly start
data = data.asfreq('QS')      # Quarterly start

"ValueError: y contains NaN values"

# Fix 1 (recommended for NaN-tolerant estimators): keep NaN rows
forecaster = ForecasterRecursive(
    estimator=LGBMRegressor(verbose=-1),  # LightGBM handles NaN natively
    lags=14,
    dropna_from_series=False,  # Default — NaN rows kept in training matrices
)
forecaster.fit(y=data['target'], suppress_warnings=True)

# Fix 2: drop rows with NaN from training matrices
forecaster = ForecasterRecursive(
    estimator=RandomForestRegressor(),
    lags=14,
    dropna_from_series=True,  # Drop NaN rows before fitting
)

# Fix 3: impute missing values before fitting
data = data.ffill()                      # Forward fill
data = data.interpolate(method='linear') # Linear interpolation

"ValueError: exog must have the same index as y" / "exog does not cover forecast horizon"

# Fix: exog for prediction must cover ALL future steps
# If predicting 10 steps ahead, exog_test must have at least 10 rows
# with dates matching the expected forecast dates
exog_test = exog.loc[forecast_start:forecast_end]
predictions = forecaster.predict(steps=10, exog=exog_test)

Wrong Backtesting Function

# ❌ WRONG: using backtesting_forecaster with ForecasterStats
from skforecast.model_selection import backtesting_forecaster
backtesting_forecaster(forecaster=forecaster_stats, y=y, cv=cv, metric=metric)  # Error!

# ✅ CORRECT: use backtesting_stats for statistical models
from skforecast.model_selection import backtesting_stats
backtesting_stats(forecaster=forecaster_stats, y=y, cv=cv, metric=metric)

# ❌ WRONG: using backtesting_forecaster with ForecasterRecursiveMultiSeries
backtesting_forecaster(forecaster=forecaster_multi, y=y, cv=cv, metric=metric)  # Error!

# ✅ CORRECT: use backtesting_forecaster_multiseries
from skforecast.model_selection import backtesting_forecaster_multiseries
backtesting_forecaster_multiseries(
    forecaster=forecaster_multi, series=series, cv=cv, metric=metric
)

Wrong Search Function

# ❌ WRONG: grid_search_forecaster with ForecasterStats
grid_search_forecaster(forecaster=forecaster_stats, y=y, cv=cv, param_grid=param_grid)

# ✅ CORRECT: grid_search_stats for statistical models
from skforecast.model_selection import grid_search_stats
grid_search_stats(forecaster=forecaster_stats, y=y, cv=cv, param_grid=param_grid)

# ❌ WRONG: grid_search_forecaster with ForecasterEquivalentDate
grid_search_forecaster(forecaster=forecaster_baseline, y=y, cv=cv, param_grid=param_grid)

# ✅ CORRECT: grid_search_equivalent_date for baselines (offset/n_offsets are coupled;
#            agg_func is also searchable; pass a list of dicts to test explicit
#            configs, optional 'alias' label)
from skforecast.model_selection import grid_search_equivalent_date
grid_search_equivalent_date(
    forecaster=forecaster_baseline, y=y, cv=cv,
    param_grid=[{'offset': 1, 'n_offsets': 7}, {'offset': 7, 'n_offsets': 2}],
    metric='mean_absolute_error',
)

# ❌ WRONG: grid_search_forecaster with ForecasterRecursiveMultiSeries
grid_search_forecaster(forecaster=forecaster_multi, y=y, cv=cv, param_grid=param_grid)

# ✅ CORRECT: grid_search_forecaster_multiseries
from skforecast.model_selection import grid_search_forecaster_multiseries
grid_search_forecaster_multiseries(
    forecaster=forecaster_multi, series=series, cv=cv, param_grid=param_grid
)

# ❌ WRONG: bayesian_search_forecaster with ForecasterFoundation
bayesian_search_forecaster(forecaster=forecaster_foundation, y=y, cv=cv, search_space=search_space)

# ✅ CORRECT: bayesian_search_foundation (TimeSeriesFold only, no 'lags' in search_space)
from skforecast.model_selection import bayesian_search_foundation
bayesian_search_foundation(
    forecaster=forecaster_foundation, series=series, cv=cv,
    search_space=lambda trial: {
        'context_length': trial.suggest_categorical('context_length', [512, 2048])
    },
    metric='mean_absolute_error',
)

Prediction Interval Errors

"No in-sample residuals stored"

# ❌ WRONG: fit without residuals, then call predict_interval
forecaster.fit(y=y_train)
forecaster.predict_interval(steps=10, method='bootstrapping')

# ✅ CORRECT: store residuals during fit
forecaster.fit(y=y_train, store_in_sample_residuals=True)
forecaster.predict_interval(steps=10, method='bootstrapping')

Wrong interval method for a forecaster

ETS Model API Confusion

# ❌ WRONG (deprecated Ets API)
ets_model = Ets(error='add', trend='add', seasonal='add', seasonal_periods=12)

# ✅ CORRECT (current API)
ets_model = Ets(model='AAA', m=12)
# Model string: 1st char=Error, 2nd=Trend, 3rd=Seasonal
# A=Additive, M=Multiplicative, N=None, Z=Auto-select

Function Mapping Reference

Loading Serialized Forecasters from Older Versions

Forecasters saved (pickled/joblib) with older skforecast versions may fail to load or behave unexpectedly after upgrading. Internal attributes, class structures, and default values change between releases.

# ❌ Common error when loading a forecaster saved with an older version
import joblib
forecaster = joblib.load('forecaster_v0.13.pkl')
# AttributeError: 'ForecasterRecursive' object has no attribute 'new_attribute'
# or: ModuleNotFoundError: No module named 'skforecast.ForecasterAutoreg'

# ✅ CORRECT: retrain the forecaster with the current version
forecaster = ForecasterRecursive(
    estimator=LGBMRegressor(),
    lags=24,
)
forecaster.fit(y=y_train)
joblib.dump(forecaster, 'forecaster_v0.22.pkl')

Best practices:

• Always retrain and re-save forecasters after upgrading skforecast.
• Store training code (not just the serialized object) so models can be reproduced.
• Pin skforecast version in requirements.txt for production deployments.