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.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.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_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.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.nmrse(targets, forecasts, order=0, offset=0)[source]¶
Normalized Root Mean Squared Error
- 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, order=0, offset=0)[source]¶
Root Mean Squared Error
- Parameters
targets – array of targets
forecasts – array of forecasts
order – model order
offset – forecast offset related to target.
- 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¶
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.plot_residuals_by_model(targets, models, tam=[8, 8], 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.create_benchmark_tables(conn)[source]¶
Create a sqlite3 table designed to store benchmark results.
- Parameters
conn – a sqlite3 database connection
- 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.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.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.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.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.arima module¶
- class pyFTS.benchmarks.arima.ARIMA(**kwargs)[source]¶
Bases:
pyFTS.common.fts.FTSFaçade 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_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
pyFTS.benchmarks.knn module¶
- class pyFTS.benchmarks.knn.KNearestNeighbors(**kwargs)[source]¶
Bases:
pyFTS.common.fts.FTSA 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
pyFTS.benchmarks.naive module¶
- class pyFTS.benchmarks.naive.Naive(**kwargs)[source]¶
Bases:
pyFTS.common.fts.FTSNaïve Forecasting method
pyFTS.benchmarks.quantreg module¶
- class pyFTS.benchmarks.quantreg.QuantileRegression(**kwargs)[source]¶
Bases:
pyFTS.common.fts.FTSFaçade 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
pyFTS.benchmarks.gaussianproc module¶
pyFTS.benchmarks.BSTS module¶
- class pyFTS.benchmarks.BSTS.ARIMA(**kwargs)[source]¶
Bases:
pyFTS.common.fts.FTSFaçade 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
