pyFTS.benchmarks package

Module contents

pyFTS module for benchmarking the FTS models

Submodules

pyFTS.benchmarks.benchmarks module

Benchmarks methods for FTS methods

pyFTS.benchmarks.benchmarks.SelecaoSimples_MenorRMSE(original, parameters, modelo)[source]
pyFTS.benchmarks.benchmarks.common_process_interval_jobs(conn, data, job)[source]
pyFTS.benchmarks.benchmarks.common_process_point_jobs(conn, data, job)[source]
pyFTS.benchmarks.benchmarks.common_process_probabilistic_jobs(conn, data, job)[source]
pyFTS.benchmarks.benchmarks.common_process_time_jobs(conn, data, job)[source]
pyFTS.benchmarks.benchmarks.compareModelsPlot(original, models_fo, models_ho)[source]
pyFTS.benchmarks.benchmarks.compareModelsTable(original, models_fo, models_ho)[source]
pyFTS.benchmarks.benchmarks.distributed_model_train_test_time(models, data, windowsize, train=0.8, **kwargs)[source]

Assess the train and test times for a given list of configured models and save the results on a database.

Parameters
  • models – A list of FTS models already configured, but not yet trained,

  • data – time series data, including train and test data

  • windowsize – Train/test data windows

  • train – Percent of data window that will be used to train the models

  • kwargs

Returns

pyFTS.benchmarks.benchmarks.get_benchmark_interval_methods()[source]

Return all non FTS methods for point_to_interval forecasting

pyFTS.benchmarks.benchmarks.get_benchmark_point_methods()[source]

Return all non FTS methods for point forecasting

pyFTS.benchmarks.benchmarks.get_benchmark_probabilistic_methods()[source]

Return all FTS methods for probabilistic forecasting

pyFTS.benchmarks.benchmarks.get_interval_methods()[source]

Return all FTS methods for point_to_interval forecasting

pyFTS.benchmarks.benchmarks.get_point_methods()[source]

Return all FTS methods for point forecasting

pyFTS.benchmarks.benchmarks.get_point_multivariate_methods()[source]

Return all multivariate FTS methods por point forecasting

pyFTS.benchmarks.benchmarks.get_probabilistic_methods()[source]

Return all FTS methods for probabilistic forecasting

pyFTS.benchmarks.benchmarks.multivariate_sliding_window_benchmarks2(data, windowsize, train=0.8, **kwargs)[source]
pyFTS.benchmarks.benchmarks.mv_run_interval2(mfts, train_data, test_data, window_key=None, **kwargs)[source]
pyFTS.benchmarks.benchmarks.mv_run_point2(mfts, train_data, test_data, window_key=None, **kwargs)[source]
pyFTS.benchmarks.benchmarks.mv_run_probabilistic2(mfts, train_data, test_data, window_key=None, **kwargs)[source]
pyFTS.benchmarks.benchmarks.pftsExploreOrderAndPartitions(data, save=False, file=None)[source]
pyFTS.benchmarks.benchmarks.plotCompared(original, forecasts, labels, title)[source]
pyFTS.benchmarks.benchmarks.plot_compared_series(original, models, colors, typeonlegend=False, save=False, file=None, tam=[20, 5], points=True, intervals=True, linewidth=1.5)[source]

Plot the forecasts of several one step ahead models, by point or by interval

Parameters
  • original – Original time series data (list)

  • models – List of models to compare

  • colors – List of models colors

  • typeonlegend – Add the type of forecast (point / interval) on legend

  • save – Save the picture on file

  • file – Filename to save the picture

  • tam – Size of the picture

  • points – True to plot the point forecasts, False otherwise

  • intervals – True to plot the interval forecasts, False otherwise

  • linewidth

Returns

pyFTS.benchmarks.benchmarks.plot_point(axis, points, order, label, color='red', ls='-', linewidth=1)[source]
pyFTS.benchmarks.benchmarks.print_distribution_statistics(original, models, steps, resolution)[source]

Run probabilistic benchmarks on given models and data and print the results

Parameters
  • data – test data

  • models – a list of FTS models to benchmark

Returns

pyFTS.benchmarks.benchmarks.print_interval_statistics(original, models)[source]

Run interval benchmarks on given models and data and print the results

Parameters
  • data – test data

  • models – a list of FTS models to benchmark

Returns

pyFTS.benchmarks.benchmarks.print_point_statistics(data, models, externalmodels=None, externalforecasts=None, indexers=None)[source]

Run point benchmarks on given models and data and print the results

Parameters
  • data – test data

  • models – a list of FTS models to benchmark

  • externalmodels – a list with benchmark models (façades for other methods)

  • externalforecasts

  • indexers

Returns

pyFTS.benchmarks.benchmarks.process_interval_jobs(dataset, tag, job, conn)[source]

Extract information from an dictionary with interval benchmark results and save it on a database

Parameters
  • dataset – the benchmark dataset name

  • tag – alias for the benchmark group being executed

  • job – a dictionary with the benchmark results

  • conn – a connection to a Sqlite database

Returns

pyFTS.benchmarks.benchmarks.process_interval_jobs2(dataset, tag, job, conn)[source]
pyFTS.benchmarks.benchmarks.process_point_jobs(dataset, tag, job, conn)[source]

Extract information from a dictionary with point benchmark results and save it on a database

Parameters
  • dataset – the benchmark dataset name

  • tag – alias for the benchmark group being executed

  • job – a dictionary with the benchmark results

  • conn – a connection to a Sqlite database

Returns

pyFTS.benchmarks.benchmarks.process_point_jobs2(dataset, tag, job, conn)[source]

Extract information from a dictionary with point benchmark results and save it on a database

Parameters
  • dataset – the benchmark dataset name

  • tag – alias for the benchmark group being executed

  • job – a dictionary with the benchmark results

  • conn – a connection to a Sqlite database

Returns

pyFTS.benchmarks.benchmarks.process_probabilistic_jobs(dataset, tag, job, conn)[source]

Extract information from an dictionary with probabilistic benchmark results and save it on a database

Parameters
  • dataset – the benchmark dataset name

  • tag – alias for the benchmark group being executed

  • job – a dictionary with the benchmark results

  • conn – a connection to a Sqlite database

Returns

pyFTS.benchmarks.benchmarks.process_probabilistic_jobs2(dataset, tag, job, conn)[source]

Extract information from an dictionary with probabilistic benchmark results and save it on a database

Parameters
  • dataset – the benchmark dataset name

  • tag – alias for the benchmark group being executed

  • job – a dictionary with the benchmark results

  • conn – a connection to a Sqlite database

Returns

pyFTS.benchmarks.benchmarks.run_interval(mfts, partitioner, train_data, test_data, window_key=None, **kwargs)[source]

Run the interval forecasting benchmarks

Parameters
  • mfts – FTS model

  • partitioner – Universe of Discourse partitioner

  • train_data – data used to train the model

  • test_data – ata used to test the model

  • window_key – id of the sliding window

  • transformation – data transformation

  • indexer – seasonal indexer

Returns

a dictionary with the benchmark results

pyFTS.benchmarks.benchmarks.run_interval2(fts_method, order, partitioner_method, partitions, transformation, train_data, test_data, window_key=None, **kwargs)[source]
pyFTS.benchmarks.benchmarks.run_point(mfts, partitioner, train_data, test_data, window_key=None, **kwargs)[source]

Run the point forecasting benchmarks

Parameters
  • mfts – FTS model

  • partitioner – Universe of Discourse partitioner

  • train_data – data used to train the model

  • test_data – ata used to test the model

  • window_key – id of the sliding window

  • transformation – data transformation

  • indexer – seasonal indexer

Returns

a dictionary with the benchmark results

pyFTS.benchmarks.benchmarks.run_point2(fts_method, order, partitioner_method, partitions, transformation, train_data, test_data, window_key=None, **kwargs)[source]
pyFTS.benchmarks.benchmarks.run_probabilistic(mfts, partitioner, train_data, test_data, window_key=None, **kwargs)[source]

Run the probabilistic forecasting benchmarks

Parameters
  • mfts – FTS model

  • partitioner – Universe of Discourse partitioner

  • train_data – data used to train the model

  • test_data – ata used to test the model

  • steps

  • resolution

  • window_key – id of the sliding window

  • transformation – data transformation

  • indexer – seasonal indexer

Returns

a dictionary with the benchmark results

pyFTS.benchmarks.benchmarks.run_probabilistic2(fts_method, order, partitioner_method, partitions, transformation, train_data, test_data, window_key=None, **kwargs)[source]
pyFTS.benchmarks.benchmarks.simpleSearch_RMSE(train, test, model, partitions, orders, save=False, file=None, tam=[10, 15], plotforecasts=False, elev=30, azim=144, intervals=False, parameters=None, partitioner=<class 'pyFTS.partitioners.Grid.GridPartitioner'>, transformation=None, indexer=None)[source]
pyFTS.benchmarks.benchmarks.sliding_window_benchmarks(data, windowsize, train=0.8, **kwargs)[source]

Sliding window benchmarks for FTS forecasters.

For each data window, a train and test datasets will be splitted. For each train split, number of partitions and partitioning method will be created a partitioner model. And for each partitioner, order, steps ahead and FTS method a foreasting model will be trained.

Then all trained models are benchmarked on the test data and the metrics are stored on a sqlite3 database (identified by the ‘file’ parameter) for posterior analysis.

All these process can be distributed on a dispy cluster, setting the atributed ‘distributed’ to true and informing the list of dispy nodes on ‘nodes’ parameter.

The number of experiments is determined by ‘windowsize’ and ‘inc’ parameters.

Parameters
  • data – test data

  • windowsize – size of sliding window

  • train – percentual of sliding window data used to train the models

  • kwargs – dict, optional arguments

  • benchmark_methods – a list with Non FTS models to benchmark. The default is None.

  • benchmark_methods_parameters – a list with Non FTS models parameters. The default is None.

  • benchmark_models – A boolean value indicating if external FTS methods will be used on benchmark. The default is False.

  • build_methods – A boolean value indicating if the default FTS methods will be used on benchmark. The default is True.

  • dataset – the dataset name to identify the current set of benchmarks results on database.

  • distributed – A boolean value indicating if the forecasting procedure will be distributed in a dispy cluster. . The default is False

  • file – file path to save the results. The default is benchmarks.db.

  • inc – a float on interval [0,1] indicating the percentage of the windowsize to move the window

  • methods – a list with FTS class names. The default depends on the forecasting type and contains the list of all FTS methods.

  • models – a list with prebuilt FTS objects. The default is None.

  • nodes – a list with the dispy cluster nodes addresses. The default is [127.0.0.1].

  • orders – a list with orders of the models (for high order models). The default is [1,2,3].

  • partitions – a list with the numbers of partitions on the Universe of Discourse. The default is [10].

  • partitioners_models – a list with prebuilt Universe of Discourse partitioners objects. The default is None.

  • partitioners_methods – a list with Universe of Discourse partitioners class names. The default is [partitioners.Grid.GridPartitioner].

  • progress – If true a progress bar will be displayed during the benchmarks. The default is False.

  • start – in the multi step forecasting, the index of the data where to start forecasting. The default is 0.

  • steps_ahead – a list with the forecasting horizons, i. e., the number of steps ahead to forecast. The default is 1.

  • tag – a name to identify the current set of benchmarks results on database.

  • type – the forecasting type, one of these values: point(default), interval or distribution. The default is point.

  • transformations – a list with data transformations do apply . The default is [None].

pyFTS.benchmarks.benchmarks.sliding_window_benchmarks2(data, windowsize, train=0.8, **kwargs)[source]
pyFTS.benchmarks.benchmarks.train_test_time(data, windowsize, train=0.8, **kwargs)[source]

pyFTS.benchmarks.Measures module

pyFTS module for common benchmark metrics

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 Score for probabilistic forecasts. Brier (1950). “Verification of Forecasts Expressed in Terms of Probability”. Monthly Weather Review. 78: 1–3.

Parameters
  • targets – a list with the target values

  • densities – a list with pyFTS.probabil objectsistic.ProbabilityDistribution

Returns

float

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_ahead_statistics(data, distributions)[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_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_ahead_statistics(data, intervals, **kwargs)[source]

Condensate all measures for point interval forecasters

Parameters
  • data – test data

  • intervals – predicted intervals for each datapoint

  • 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_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_ahead_statistics(data, forecasts, **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.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.logarithm_score(targets, densities)[source]

Logarithm Score for probabilistic forecasts. Good IJ (1952). “Rational Decisions.”Journal of the Royal Statistical Society B,14(1),107–114. URLhttps://www.jstor.org/stable/2984087.

Parameters
  • targets – a list with the target values

  • densities – a list with pyFTS.probabil objectsistic.ProbabilityDistribution

Returns

float

pyFTS.benchmarks.Measures.mape(targets, forecasts)[source]

Mean Average Percentual Error

Parameters
  • targets

  • forecasts

Returns

pyFTS.benchmarks.Measures.mape_interval(targets, forecasts)[source]
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.sharpness(forecasts)[source]

Sharpness - Mean size of the intervals

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.Measures.winkler_score(tau, target, forecast)[source]
    1. Winkler, A Decision-Theoretic Approach to Interval Estimation, J. Am. Stat. Assoc. 67 (337) (1972) 187–191. doi:10.2307/2284720.

Parameters
  • tau

  • target

  • forecast

Returns

pyFTS.benchmarks.ResidualAnalysis module

Residual Analysis methods

pyFTS.benchmarks.ResidualAnalysis.compare_residuals(data, models, alpha=0.05)[source]

Compare residual’s statistics of several models

Parameters
  • data – test data

  • models

Returns

a Pandas dataframe with the Box-Ljung statistic for each model

pyFTS.benchmarks.ResidualAnalysis.ljung_box_test(residuals, lags=[1, 2, 3], alpha=0.5)[source]
pyFTS.benchmarks.ResidualAnalysis.plot_residuals_by_model(targets, models, tam=[8, 8], save=False, file=None)[source]
pyFTS.benchmarks.ResidualAnalysis.residuals(targets, forecasts, order=1)[source]

First order residuals

pyFTS.benchmarks.ResidualAnalysis.single_plot_residuals(res, order, tam=[10, 7], save=False, file=None)[source]

pyFTS.benchmarks.Tests module

pyFTS.benchmarks.Tests.BoxLjungStatistic(data, h)[source]

Q Statistic for Ljung–Box test

Parameters
  • data

  • h

Returns

pyFTS.benchmarks.Tests.BoxPierceStatistic(data, h)[source]

Q Statistic for Box-Pierce test

Parameters
  • data

  • h

Returns

pyFTS.benchmarks.Tests.format_experiment_table(df, exclude=[], replace={}, csv=True, std=False)[source]
pyFTS.benchmarks.Tests.post_hoc_tests(post_hoc, control_method, alpha=0.05, method='finner')[source]

Finner paired post-hoc test with NSFTS as control method.

$H_0$: There is no significant difference between the means

$H_1$: There is a significant difference between the means

Parameters
  • post_hoc

  • control_method

  • alpha

  • method

Returns

pyFTS.benchmarks.Tests.test_mean_equality(tests, alpha=0.05, method='friedman')[source]

Test for the equality of the means, with alpha confidence level.

H_0: There’s no significant difference between the means H_1: There is at least one significant difference between the means

Parameters
  • tests

  • alpha

  • method

Returns

pyFTS.benchmarks.Util module

Facilities for pyFTS Benchmark module

pyFTS.benchmarks.Util.analytic_tabular_dataframe(dataframe)[source]
pyFTS.benchmarks.Util.analytical_data_columns(experiments)[source]
pyFTS.benchmarks.Util.base_dataframe_columns()[source]
pyFTS.benchmarks.Util.cast_dataframe_to_synthetic(infile, outfile, experiments, type)[source]
pyFTS.benchmarks.Util.cast_dataframe_to_synthetic_interval(df, data_columns)[source]
pyFTS.benchmarks.Util.cast_dataframe_to_synthetic_point(df, data_columns)[source]
pyFTS.benchmarks.Util.cast_dataframe_to_synthetic_probabilistic(df, data_columns)[source]
pyFTS.benchmarks.Util.check_ignore_list(b, ignore)[source]
pyFTS.benchmarks.Util.check_replace_list(m, replace)[source]
pyFTS.benchmarks.Util.create_benchmark_tables(conn)[source]

Create a sqlite3 table designed to store benchmark results.

Parameters

conn – a sqlite3 database connection

pyFTS.benchmarks.Util.extract_measure(dataframe, measure, data_columns)[source]
pyFTS.benchmarks.Util.find_best(dataframe, criteria, ascending)[source]
pyFTS.benchmarks.Util.get_dataframe_from_bd(file, filter)[source]

Query the sqlite benchmark database and return a pandas dataframe with the results

Parameters
  • file – the url of the benchmark database

  • filter – sql conditions to filter

Returns

pandas dataframe with the query results

pyFTS.benchmarks.Util.insert_benchmark(data, conn)[source]

Insert benchmark data on database

Parameters

data – a tuple with the benchmark data with format:

ID: integer incremental primary key Date: Date/hour of benchmark execution Dataset: Identify on which dataset the dataset was performed Tag: a user defined word that indentify a benchmark set Type: forecasting type (point, interval, distribution) Model: FTS model Transformation: The name of data transformation, if one was used Order: the order of the FTS method Scheme: UoD partitioning scheme Partitions: Number of partitions Size: Number of rules of the FTS model Steps: prediction horizon, i. e., the number of steps ahead Measure: accuracy measure Value: the measure value

Parameters

conn – a sqlite3 database connection

Returns

pyFTS.benchmarks.Util.interval_dataframe_analytic_columns(experiments)[source]
pyFTS.benchmarks.Util.interval_dataframe_synthetic_columns()[source]
pyFTS.benchmarks.Util.open_benchmark_db(name)[source]

Open a connection with a Sqlite database designed to store benchmark results.

Parameters

name – database filenem

Returns

a sqlite3 database connection

pyFTS.benchmarks.Util.plot_dataframe_interval(file_synthetic, file_analytic, experiments, tam, save=False, file=None, sort_columns=['COVAVG', 'SHARPAVG', 'COVSTD', 'SHARPSTD'], sort_ascend=[True, False, True, True], save_best=False, ignore=None, replace=None)[source]
pyFTS.benchmarks.Util.plot_dataframe_interval_pinball(file_synthetic, file_analytic, experiments, tam, save=False, file=None, sort_columns=['COVAVG', 'SHARPAVG', 'COVSTD', 'SHARPSTD'], sort_ascend=[True, False, True, True], save_best=False, ignore=None, replace=None)[source]
pyFTS.benchmarks.Util.plot_dataframe_point(file_synthetic, file_analytic, experiments, tam, save=False, file=None, sort_columns=['UAVG', 'RMSEAVG', 'USTD', 'RMSESTD'], sort_ascend=[1, 1, 1, 1], save_best=False, ignore=None, replace=None)[source]
pyFTS.benchmarks.Util.plot_dataframe_probabilistic(file_synthetic, file_analytic, experiments, tam, save=False, file=None, sort_columns=['CRPS1AVG', 'CRPS2AVG', 'CRPS1STD', 'CRPS2STD'], sort_ascend=[True, True, True, True], save_best=False, ignore=None, replace=None)[source]
pyFTS.benchmarks.Util.point_dataframe_analytic_columns(experiments)[source]
pyFTS.benchmarks.Util.point_dataframe_synthetic_columns()[source]
pyFTS.benchmarks.Util.probabilistic_dataframe_analytic_columns(experiments)[source]
pyFTS.benchmarks.Util.probabilistic_dataframe_synthetic_columns()[source]
pyFTS.benchmarks.Util.process_common_data(dataset, tag, type, job)[source]

Wraps benchmark information on a tuple for sqlite database

Parameters
  • dataset – benchmark dataset

  • tag – benchmark set alias

  • type – forecasting type

  • job – a dictionary with benchmark data

Returns

tuple for sqlite database

pyFTS.benchmarks.Util.process_common_data2(dataset, tag, type, job)[source]

Wraps benchmark information on a tuple for sqlite database

Parameters
  • dataset – benchmark dataset

  • tag – benchmark set alias

  • type – forecasting type

  • job – a dictionary with benchmark data

Returns

tuple for sqlite database

pyFTS.benchmarks.Util.save_dataframe_interval(coverage, experiments, file, objs, resolution, save, sharpness, synthetic, times, q05, q25, q75, q95, steps, method)[source]
pyFTS.benchmarks.Util.save_dataframe_point(experiments, file, objs, rmse, save, synthetic, smape, times, u, steps, method)[source]

Create a dataframe to store the benchmark results

Parameters
  • experiments – dictionary with the execution results

  • file

  • objs

  • rmse

  • save

  • synthetic

  • smape

  • times

  • u

Returns

pyFTS.benchmarks.Util.save_dataframe_probabilistic(experiments, file, objs, crps, times, save, synthetic, steps, method)[source]

Save benchmark results for m-step ahead probabilistic forecasters :param experiments: :param file: :param objs: :param crps_interval: :param crps_distr: :param times: :param times2: :param save: :param synthetic: :return:

pyFTS.benchmarks.Util.scale(data, params)[source]
pyFTS.benchmarks.Util.scale_params(data)[source]
pyFTS.benchmarks.Util.simple_synthetic_dataframe(file, tag, measure, sql=None)[source]

Read experiments results from sqlite3 database in ‘file’, make a synthesis of the results of the metric ‘measure’ with the same ‘tag’, returning a Pandas DataFrame with the mean results.

Parameters
  • file – sqlite3 database file name

  • tag – common tag of the experiments

  • measure – metric to synthetize

Returns

Pandas DataFrame with the mean results

pyFTS.benchmarks.Util.stats(measure, data)[source]
pyFTS.benchmarks.Util.tabular_dataframe_columns()[source]
pyFTS.benchmarks.Util.unified_scaled_interval(experiments, tam, save=False, file=None, sort_columns=['COVAVG', 'SHARPAVG', 'COVSTD', 'SHARPSTD'], sort_ascend=[True, False, True, True], save_best=False, ignore=None, replace=None)[source]
pyFTS.benchmarks.Util.unified_scaled_interval_pinball(experiments, tam, save=False, file=None, sort_columns=['COVAVG', 'SHARPAVG', 'COVSTD', 'SHARPSTD'], sort_ascend=[True, False, True, True], save_best=False, ignore=None, replace=None)[source]
pyFTS.benchmarks.Util.unified_scaled_point(experiments, tam, save=False, file=None, sort_columns=['UAVG', 'RMSEAVG', 'USTD', 'RMSESTD'], sort_ascend=[1, 1, 1, 1], save_best=False, ignore=None, replace=None)[source]
pyFTS.benchmarks.Util.unified_scaled_probabilistic(experiments, tam, save=False, file=None, sort_columns=['CRPSAVG', 'CRPSSTD'], sort_ascend=[True, True], save_best=False, ignore=None, replace=None)[source]

pyFTS.benchmarks.arima module

class pyFTS.benchmarks.arima.ARIMA(**kwargs)[source]

Bases: pyFTS.common.fts.FTS

Façade for statsmodels.tsa.arima_model

ar(data)[source]
forecast(ndata, **kwargs)[source]

Point forecast one step ahead

Parameters
  • data – time series data with the minimal length equal to the max_lag of the model

  • kwargs – model specific parameters

Returns

a list with the forecasted values

forecast_ahead_distribution(data, steps, **kwargs)[source]

Probabilistic forecast from 1 to H steps ahead, where H is given by the steps parameter

Parameters
  • data – time series data with the minimal length equal to the max_lag of the model

  • steps – the number of steps ahead to forecast

  • start_at – in the multi step forecasting, the index of the data where to start forecasting (default: 0)

Returns

a list with the forecasted Probability Distributions

forecast_ahead_interval(ndata, steps, **kwargs)[source]

Interval forecast from 1 to H steps ahead, where H is given by the steps parameter

Parameters
  • data – time series data with the minimal length equal to the max_lag of the model

  • steps – the number of steps ahead to forecast

  • start_at – in the multi step forecasting, the index of the data where to start forecasting (default: 0)

Returns

a list with the forecasted intervals

forecast_distribution(data, **kwargs)[source]

Probabilistic forecast one step ahead

Parameters
  • data – time series data with the minimal length equal to the max_lag of the model

  • kwargs – model specific parameters

Returns

a list with probabilistic.ProbabilityDistribution objects representing the forecasted Probability Distributions

forecast_interval(data, **kwargs)[source]

Interval forecast one step ahead

Parameters
  • data – time series data with the minimal length equal to the max_lag of the model

  • kwargs – model specific parameters

Returns

a list with the prediction intervals

ma(data)[source]
train(data, **kwargs)[source]

Method specific parameter fitting

Parameters
  • data – training time series data

  • kwargs – Method specific parameters

pyFTS.benchmarks.knn module

class pyFTS.benchmarks.knn.KNearestNeighbors(**kwargs)[source]

Bases: pyFTS.common.fts.FTS

A façade for sklearn.neighbors

forecast(data, **kwargs)[source]

Point forecast one step ahead

Parameters
  • data – time series data with the minimal length equal to the max_lag of the model

  • kwargs – model specific parameters

Returns

a list with the forecasted values

forecast_ahead_distribution(data, steps, **kwargs)[source]

Probabilistic forecast from 1 to H steps ahead, where H is given by the steps parameter

Parameters
  • data – time series data with the minimal length equal to the max_lag of the model

  • steps – the number of steps ahead to forecast

  • start_at – in the multi step forecasting, the index of the data where to start forecasting (default: 0)

Returns

a list with the forecasted Probability Distributions

forecast_ahead_interval(data, steps, **kwargs)[source]

Interval forecast from 1 to H steps ahead, where H is given by the steps parameter

Parameters
  • data – time series data with the minimal length equal to the max_lag of the model

  • steps – the number of steps ahead to forecast

  • start_at – in the multi step forecasting, the index of the data where to start forecasting (default: 0)

Returns

a list with the forecasted intervals

forecast_distribution(data, **kwargs)[source]

Probabilistic forecast one step ahead

Parameters
  • data – time series data with the minimal length equal to the max_lag of the model

  • kwargs – model specific parameters

Returns

a list with probabilistic.ProbabilityDistribution objects representing the forecasted Probability Distributions

forecast_interval(data, **kwargs)[source]

Interval forecast one step ahead

Parameters
  • data – time series data with the minimal length equal to the max_lag of the model

  • kwargs – model specific parameters

Returns

a list with the prediction intervals

knn(sample)[source]
train(data, **kwargs)[source]

Method specific parameter fitting

Parameters
  • data – training time series data

  • kwargs – Method specific parameters

pyFTS.benchmarks.naive module

class pyFTS.benchmarks.naive.Naive(**kwargs)[source]

Bases: pyFTS.common.fts.FTS

Naïve Forecasting method

forecast(data, **kwargs)[source]

Point forecast one step ahead

Parameters
  • data – time series data with the minimal length equal to the max_lag of the model

  • kwargs – model specific parameters

Returns

a list with the forecasted values

pyFTS.benchmarks.quantreg module

class pyFTS.benchmarks.quantreg.QuantileRegression(**kwargs)[source]

Bases: pyFTS.common.fts.FTS

Façade for statsmodels.regression.quantile_regression

forecast(ndata, **kwargs)[source]

Point forecast one step ahead

Parameters
  • data – time series data with the minimal length equal to the max_lag of the model

  • kwargs – model specific parameters

Returns

a list with the forecasted values

forecast_ahead_distribution(ndata, steps, **kwargs)[source]

Probabilistic forecast from 1 to H steps ahead, where H is given by the steps parameter

Parameters
  • data – time series data with the minimal length equal to the max_lag of the model

  • steps – the number of steps ahead to forecast

  • start_at – in the multi step forecasting, the index of the data where to start forecasting (default: 0)

Returns

a list with the forecasted Probability Distributions

forecast_ahead_interval(ndata, steps, **kwargs)[source]

Interval forecast from 1 to H steps ahead, where H is given by the steps parameter

Parameters
  • data – time series data with the minimal length equal to the max_lag of the model

  • steps – the number of steps ahead to forecast

  • start_at – in the multi step forecasting, the index of the data where to start forecasting (default: 0)

Returns

a list with the forecasted intervals

forecast_distribution(ndata, **kwargs)[source]

Probabilistic forecast one step ahead

Parameters
  • data – time series data with the minimal length equal to the max_lag of the model

  • kwargs – model specific parameters

Returns

a list with probabilistic.ProbabilityDistribution objects representing the forecasted Probability Distributions

forecast_interval(ndata, **kwargs)[source]

Interval forecast one step ahead

Parameters
  • data – time series data with the minimal length equal to the max_lag of the model

  • kwargs – model specific parameters

Returns

a list with the prediction intervals

interval_to_interval(data, lo_params, up_params)[source]
linearmodel(data, params)[source]
point_to_interval(data, lo_params, up_params)[source]
train(data, **kwargs)[source]

Method specific parameter fitting

Parameters
  • data – training time series data

  • kwargs – Method specific parameters

pyFTS.benchmarks.gaussianproc module

pyFTS.benchmarks.BSTS module

class pyFTS.benchmarks.BSTS.ARIMA(**kwargs)[source]

Bases: pyFTS.common.fts.FTS

Façade for statsmodels.tsa.arima_model

forecast(ndata, **kwargs)[source]

Point forecast one step ahead

Parameters
  • data – time series data with the minimal length equal to the max_lag of the model

  • kwargs – model specific parameters

Returns

a list with the forecasted values

forecast_ahead(data, steps, **kwargs)[source]

Point forecast from 1 to H steps ahead, where H is given by the steps parameter

Parameters
  • data – time series data with the minimal length equal to the max_lag of the model

  • steps – the number of steps ahead to forecast (default: 1)

  • start_at – in the multi step forecasting, the index of the data where to start forecasting (default: 0)

Returns

a list with the forecasted values

forecast_ahead_distribution(data, steps, **kwargs)[source]

Probabilistic forecast from 1 to H steps ahead, where H is given by the steps parameter

Parameters
  • data – time series data with the minimal length equal to the max_lag of the model

  • steps – the number of steps ahead to forecast

  • start_at – in the multi step forecasting, the index of the data where to start forecasting (default: 0)

Returns

a list with the forecasted Probability Distributions

forecast_ahead_interval(ndata, steps, **kwargs)[source]

Interval forecast from 1 to H steps ahead, where H is given by the steps parameter

Parameters
  • data – time series data with the minimal length equal to the max_lag of the model

  • steps – the number of steps ahead to forecast

  • start_at – in the multi step forecasting, the index of the data where to start forecasting (default: 0)

Returns

a list with the forecasted intervals

forecast_distribution(data, **kwargs)[source]

Probabilistic forecast one step ahead

Parameters
  • data – time series data with the minimal length equal to the max_lag of the model

  • kwargs – model specific parameters

Returns

a list with probabilistic.ProbabilityDistribution objects representing the forecasted Probability Distributions

forecast_interval(data, **kwargs)[source]

Interval forecast one step ahead

Parameters
  • data – time series data with the minimal length equal to the max_lag of the model

  • kwargs – model specific parameters

Returns

a list with the prediction intervals

inference(steps)[source]
train(data, **kwargs)[source]

Method specific parameter fitting

Parameters
  • data – training time series data

  • kwargs – Method specific parameters