pyFTS.benchmarks package¶
Submodules¶
pyFTS.benchmarks.Measures module¶
pyFTS module for common benchmark metrics
-
pyFTS.benchmarks.Measures.
BoxLjungStatistic
(data, h)[source]¶ Q Statistic for Ljung–Box test
Parameters: - data –
- h –
Returns:
-
pyFTS.benchmarks.Measures.
BoxPierceStatistic
(data, h)[source]¶ Q Statistic for Box-Pierce test
Parameters: - data –
- h –
Returns:
-
pyFTS.benchmarks.Measures.
TheilsInequality
(targets, forecasts)[source]¶ Theil’s Inequality Coefficient
Parameters: - targets –
- forecasts –
Returns:
-
pyFTS.benchmarks.Measures.
UStatistic
(targets, forecasts)[source]¶ Theil’s U Statistic
Parameters: - targets –
- forecasts –
Returns:
-
pyFTS.benchmarks.Measures.
acf
(data, k)[source]¶ Autocorrelation function estimative
Parameters: - data –
- k –
Returns:
-
pyFTS.benchmarks.Measures.
brier_score
(targets, densities)[source]¶ Brier (1950). “Verification of Forecasts Expressed in Terms of Probability”. Monthly Weather Review. 78: 1–3.
-
pyFTS.benchmarks.Measures.
coverage
(targets, forecasts)[source]¶ Percent of target values that fall inside forecasted interval
-
pyFTS.benchmarks.Measures.
crps
(targets, densities)[source]¶ Continuous Ranked Probability Score
Parameters: - targets – a list with the target values
- densities – a list with pyFTS.probabil objectsistic.ProbabilityDistribution
Returns: float
-
pyFTS.benchmarks.Measures.
get_distribution_statistics
(data, model, **kwargs)[source]¶ Get CRPS statistic and time for a forecasting model
Parameters: - data – test data
- model – FTS model with probabilistic forecasting capability
- kwargs –
Returns: a list with the CRPS and execution time
-
pyFTS.benchmarks.Measures.
get_interval_statistics
(data, model, **kwargs)[source]¶ Condensate all measures for point interval forecasters
Parameters: - data – test data
- model – FTS model with interval forecasting capability
- kwargs –
Returns: a list with the sharpness, resolution, coverage, .05 pinball mean,
.25 pinball mean, .75 pinball mean and .95 pinball mean.
-
pyFTS.benchmarks.Measures.
get_point_statistics
(data, model, **kwargs)[source]¶ Condensate all measures for point forecasters
Parameters: - data – test data
- model – FTS model with point forecasting capability
- kwargs –
Returns: a list with the RMSE, SMAPE and U Statistic
-
pyFTS.benchmarks.Measures.
mape
(targets, forecasts)[source]¶ Mean Average Percentual Error
Parameters: - targets –
- forecasts –
Returns:
-
pyFTS.benchmarks.Measures.
pinball
(tau, target, forecast)[source]¶ Pinball loss function. Measure the distance of forecast to the tau-quantile of the target
Parameters: - tau – quantile value in the range (0,1)
- target –
- forecast –
Returns: float, distance of forecast to the tau-quantile of the target
-
pyFTS.benchmarks.Measures.
pinball_mean
(tau, targets, forecasts)[source]¶ Mean pinball loss value of the forecast for a given tau-quantile of the targets
Parameters: - tau – quantile value in the range (0,1)
- targets – list of target values
- forecasts – list of prediction intervals
Returns: float, the pinball loss mean for tau quantile
-
pyFTS.benchmarks.Measures.
resolution
(forecasts)[source]¶ Resolution - Standard deviation of the intervals
-
pyFTS.benchmarks.Measures.
rmse
(targets, forecasts)[source]¶ Root Mean Squared Error
Parameters: - targets –
- forecasts –
Returns:
-
pyFTS.benchmarks.Measures.
rmse_interval
(targets, forecasts)[source]¶ Root Mean Squared Error
Parameters: - targets –
- forecasts –
Returns:
-
pyFTS.benchmarks.Measures.
smape
(targets, forecasts, type=2)[source]¶ Symmetric Mean Average Percentual Error
Parameters: - targets –
- forecasts –
- type –
Returns:
-
pyFTS.benchmarks.Measures.
winkler_mean
(tau, targets, forecasts)[source]¶ Mean Winkler score value of the forecast for a given tau-quantile of the targets
Parameters: - tau – quantile value in the range (0,1)
- targets – list of target values
- forecasts – list of prediction intervals
Returns: float, the Winkler score mean for tau quantile
pyFTS.benchmarks.ResidualAnalysis module¶
pyFTS.benchmarks.Util module¶
pyFTS.benchmarks.arima module¶
pyFTS.benchmarks.benchmarks module¶
pyFTS.benchmarks.distributed_benchmarks module¶
pyFTS.benchmarks.knn module¶
pyFTS.benchmarks.naive module¶
pyFTS.benchmarks.parallel_benchmarks module¶
pyFTS.benchmarks.quantreg module¶
Module contents¶
pyFTS module for benchmarking the FTS models