get_UoD() (pyFTS.common.fts.FTS method)
|
pseudologlikelihood() (pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution method)
+
+ pwflrg_lhs_memberhip_fuzzyfied() (pyFTS.models.pwfts.ProbabilisticWeightedFTS method)
pyFTS (module)
@@ -2025,6 +2061,8 @@
|
- train_individual_model() (in module pyFTS.models.ensemble.multiseasonal)
+
+ - train_test_time() (in module pyFTS.benchmarks.benchmarks)
- transform() (pyFTS.common.Composite.FuzzySet method)
diff --git a/docs/build/html/objects.inv b/docs/build/html/objects.inv
index 0d04302..8b3b2f1 100644
Binary files a/docs/build/html/objects.inv and b/docs/build/html/objects.inv differ
diff --git a/docs/build/html/pyFTS.benchmarks.html b/docs/build/html/pyFTS.benchmarks.html
index 77afebb..1318617 100644
--- a/docs/build/html/pyFTS.benchmarks.html
+++ b/docs/build/html/pyFTS.benchmarks.html
@@ -145,6 +145,11 @@
pyFTS.benchmarks.benchmarks.common_process_probabilistic_jobs(conn, data, job)
+
+-
+
pyFTS.benchmarks.benchmarks.common_process_time_jobs(conn, data, job)
+
+
-
pyFTS.benchmarks.benchmarks.compareModelsPlot(original, models_fo, models_ho)
@@ -581,6 +586,11 @@ informing the list of dispy nodes on ‘nodes’ parameter.
pyFTS.benchmarks.benchmarks.sliding_window_benchmarks2(data, windowsize, train=0.8, **kwargs)
+
+-
+
pyFTS.benchmarks.benchmarks.train_test_time(data, windowsize, train=0.8, **kwargs)
+
+
pyFTS.benchmarks.Measures module
@@ -751,7 +761,7 @@ Brier (1950). “Verification of Forecasts Expressed in Terms of Probability”.
| Parameters: |
- data – test data
-- model – FTS model with interval forecasting capability
+- intervals – predicted intervals for each datapoint
- kwargs –
|
@@ -787,6 +797,28 @@ Brier (1950). “Verification of Forecasts Expressed in Terms of Probability”.
+
+-
+
pyFTS.benchmarks.Measures.get_point_ahead_statistics(data, forecasts, **kwargs)
+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)
@@ -1030,30 +1062,9 @@ Good IJ (1952). “Rational Decisions.”Journal of the Royal Statistical Socie
pyFTS.benchmarks.ResidualAnalysis module
Residual Analysis methods
-
--
-
pyFTS.benchmarks.ResidualAnalysis.chi_squared(q, h)
-Chi-Squared value
-
-
-
-
-| Parameters: |
- |
-
|---|
-| Returns: |
- |
-
|---|
-
-
-
-
-
-
pyFTS.benchmarks.ResidualAnalysis.compare_residuals(data, models)
+pyFTS.benchmarks.ResidualAnalysis.compare_residuals(data, models, alpha=0.05)
Compare residual’s statistics of several models
@@ -1073,32 +1084,13 @@ Good IJ (1952). “Rational Decisions.”Journal of the Royal Statistical Socie
--
-
pyFTS.benchmarks.ResidualAnalysis.plotResiduals(targets, models, tam=[8, 8], save=False, file=None)
-Plot residuals and statistics
-
-
-
-
-| Parameters: |
-- targets –
-- models –
-- tam –
-- save –
-- file –
-
- |
-
|---|
-| Returns: |
- |
-
|---|
-
-
-
+-
+
pyFTS.benchmarks.ResidualAnalysis.ljung_box_test(residuals, lags=[1, 2, 3], alpha=0.5)
+
--
-
pyFTS.benchmarks.ResidualAnalysis.plot_residuals(targets, models, tam=[8, 8], save=False, file=None)
+-
+
pyFTS.benchmarks.ResidualAnalysis.plot_residuals_by_model(targets, models, tam=[8, 8], save=False, file=None)
@@ -1109,7 +1101,7 @@ Good IJ (1952). “Rational Decisions.”Journal of the Royal Statistical Socie
-
-
pyFTS.benchmarks.ResidualAnalysis.single_plot_residuals(targets, forecasts, order, tam=[8, 8], save=False, file=None)
+pyFTS.benchmarks.ResidualAnalysis.single_plot_residuals(res, order, tam=[10, 7], save=False, file=None)
@@ -1741,28 +1733,6 @@ of the metric ‘measure’ with the same ‘tag’, returning a Pandas DataFram
-
--
-
forecast_ahead(data, steps, **kwargs)
-Point forecast n steps ahead
-
-
-
-
-| 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)
@@ -2241,6 +2211,28 @@ of the metric ‘measure’ with the same ‘tag’, returning a Pandas DataFram
+
+-
+
forecast_ahead(data, steps, **kwargs)
+Point forecast n steps ahead
+
+
+
+
+| 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)
diff --git a/docs/build/html/pyFTS.models.html b/docs/build/html/pyFTS.models.html
index 3c39b15..6df690a 100644
--- a/docs/build/html/pyFTS.models.html
+++ b/docs/build/html/pyFTS.models.html
@@ -941,6 +941,11 @@ US Dollar to Ringgit Malaysia,” Int. J. Comput. Intell. Appl., vol. 12, no. 1,
lhs_conditional_probability(x, sets, norm, uod, nbins)
+
+-
+
lhs_conditional_probability_fuzzyfied(lhs_mv, sets, norm, uod, nbins)
+
+
-
partition_function(sets, uod, nbins=100)
@@ -973,6 +978,11 @@ US Dollar to Ringgit Malaysia,” Int. J. Comput. Intell. Appl., vol. 12, no. 1,
flrg_lhs_conditional_probability(x, flrg)
+
+-
+
flrg_lhs_conditional_probability_fuzzyfied(x, flrg)
+
+
-
flrg_lhs_unconditional_probability(flrg)
@@ -1117,6 +1127,16 @@ US Dollar to Ringgit Malaysia,” Int. J. Comput. Intell. Appl., vol. 12, no. 1,
generate_flrg(data)
+
+-
+
generate_flrg2(data)
+
+
+
+-
+
generate_flrg_fuzzyfied(data)
+
+
-
generate_lhs_flrg(sample, explain=False)
@@ -1137,6 +1157,11 @@ US Dollar to Ringgit Malaysia,” Int. J. Comput. Intell. Appl., vol. 12, no. 1,
get_midpoint(flrg)
+
+-
+
get_sets_from_both_fuzzyfication(sample)
+
+
-
get_upper(flrg)
@@ -1144,12 +1169,12 @@ US Dollar to Ringgit Malaysia,” Int. J. Comput. Intell. Appl., vol. 12, no. 1,
-
-
interval_heuristic(sample)
+interval_heuristic(sample, **kwargs)
-
-
interval_quantile(ndata, alpha)
+interval_quantile(ndata, alpha, **kwargs)
@@ -1162,6 +1187,11 @@ US Dollar to Ringgit Malaysia,” Int. J. Comput. Intell. Appl., vol. 12, no. 1,
point_heuristic(sample, **kwargs)
+
+-
+
pwflrg_lhs_memberhip_fuzzyfied(flrg, sample)
+
+
-
train(data, **kwargs)
diff --git a/docs/build/html/pyFTS.models.multivariate.html b/docs/build/html/pyFTS.models.multivariate.html
index f89e034..ba7f12f 100644
--- a/docs/build/html/pyFTS.models.multivariate.html
+++ b/docs/build/html/pyFTS.models.multivariate.html
@@ -428,6 +428,11 @@ overlapped fuzzy sets.
+
+-
+
defuzzyfy(values, mode='both')
+
+
@@ -779,6 +784,50 @@ multivariate fuzzy set base.
+
+-
+
forecast_ahead_distribution(data, steps, **kwargs)
+Probabilistic forecast n steps ahead
+
+
+
+
+| 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)
+Interval forecast n steps ahead
+
+
+
+
+| 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_ahead_multivariate(data, steps, **kwargs)
@@ -801,6 +850,48 @@ multivariate fuzzy set base.
+
+-
+
forecast_distribution(data, **kwargs)
+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)
+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
+ |
+
|---|
+
+
+
+
-
forecast_multivariate(data, **kwargs)
diff --git a/docs/build/html/pyFTS.models.seasonal.html b/docs/build/html/pyFTS.models.seasonal.html
index 4a35623..b6afd86 100644
--- a/docs/build/html/pyFTS.models.seasonal.html
+++ b/docs/build/html/pyFTS.models.seasonal.html
@@ -600,6 +600,12 @@
build_index()
+
+
+Extract a single primitive type from an structured instance
+
+
-
plot(ax)
diff --git a/docs/build/html/pyFTS.partitioners.html b/docs/build/html/pyFTS.partitioners.html
index 627c3be..a087500 100644
--- a/docs/build/html/pyFTS.partitioners.html
+++ b/docs/build/html/pyFTS.partitioners.html
@@ -167,6 +167,17 @@ fuzzy set.
the data is inside the UoD.
+
+-
+
defuzzyfy(values, mode='both')
+
+
+
+
+Extract a single primitive type from an structured instance
+
+
-
fuzzyfy(data, **kwargs)
diff --git a/docs/build/html/searchindex.js b/docs/build/html/searchindex.js
index 31f8861..d68ae37 100644
--- a/docs/build/html/searchindex.js
+++ b/docs/build/html/searchindex.js
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