From 3db6e6911983b851d24b870c5ee61f286e257b17 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Petr=C3=B4nio=20C=C3=A2ndido?= <petronio.candido@gmail.com>
Date: Fri, 13 Apr 2018 15:23:54 -0300
Subject: [PATCH] Improvements on benchmarks

---
 img/logo_medium.png                           | Bin 0 -> 29315 bytes
 img/minds_logo.jpeg                           | Bin 0 -> 39494 bytes
 img/minds_logo_medium.jpeg                    | Bin 0 -> 15098 bytes
 pyFTS/benchmarks/Measures.py                  |  93 +++++--
 pyFTS/benchmarks/Util.py                      | 250 ++++++++----------
 pyFTS/benchmarks/benchmarks.py                |  78 ++++--
 pyFTS/common/fts.py                           |  29 +-
 pyFTS/models/pwfts.py                         |  13 +-
 .../probabilistic/ProbabilityDistribution.py  |   2 +-
 pyFTS/tests/general.py                        |  13 +-
 10 files changed, 268 insertions(+), 210 deletions(-)
 create mode 100644 img/logo_medium.png
 create mode 100644 img/minds_logo.jpeg
 create mode 100644 img/minds_logo_medium.jpeg

diff --git a/img/logo_medium.png b/img/logo_medium.png
new file mode 100644
index 0000000000000000000000000000000000000000..d2209c9ecbf0e9528ba8788f535dc1862abb90c0
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literal 0
HcmV?d00001

diff --git a/pyFTS/benchmarks/Measures.py b/pyFTS/benchmarks/Measures.py
index 8bd1a41..84c45cd 100644
--- a/pyFTS/benchmarks/Measures.py
+++ b/pyFTS/benchmarks/Measures.py
@@ -271,6 +271,8 @@ def crps(targets, densities):
 def get_point_statistics(data, model, **kwargs):
     """Condensate all measures for point forecasters"""
 
+    steps_ahead = kwargs.get('steps_ahead',1)
+
     indexer = kwargs.get('indexer', None)
 
     if indexer is not None:
@@ -278,46 +280,89 @@ def get_point_statistics(data, model, **kwargs):
     else:
         ndata = np.array(data[model.order:])
 
-    forecasts = model.predict(data, **kwargs)
+    ret = list()
 
-    try:
+    if steps_ahead == 1:
+        forecasts = model.forecast(data, **kwargs)
         if model.has_seasonality:
             nforecasts = np.array(forecasts)
         else:
             nforecasts = np.array(forecasts[:-1])
-    except Exception as ex:
-        print(ex)
-        return [np.nan,np.nan,np.nan]
-    ret = list()
+        ret.append(np.round(rmse(ndata, nforecasts), 2))
+        ret.append(np.round(smape(ndata, nforecasts), 2))
+        ret.append(np.round(UStatistic(ndata, nforecasts), 2))
+    else:
+        nforecasts = []
+        for k in np.arange(model.order, len(ndata)-steps_ahead):
+            sample = ndata[k - model.order: k]
+            tmp = model.forecast_ahead(sample, steps_ahead, **kwargs)
+            nforecasts.append(tmp[-1])
 
-    ret.append(np.round(rmse(ndata, nforecasts), 2))
-    ret.append(np.round(smape(ndata, nforecasts), 2))
-    ret.append(np.round(UStatistic(ndata, nforecasts), 2))
+        start = model.order + steps_ahead
+        ret.append(np.round(rmse(ndata[start:], nforecasts), 2))
+        ret.append(np.round(smape(ndata[start:], nforecasts), 2))
+        ret.append(np.round(UStatistic(ndata[start:], nforecasts), 2))
 
     return ret
 
 
-def get_interval_statistics(original, model, **kwargs):
+def get_interval_statistics(data, model, **kwargs):
     """Condensate all measures for point_to_interval forecasters"""
+
+    steps_ahead = kwargs.get('steps_ahead', 1)
+
     ret = list()
-    forecasts = model.predict(original, **kwargs)
-    ret.append(round(sharpness(forecasts), 2))
-    ret.append(round(resolution(forecasts), 2))
-    ret.append(round(coverage(original[model.order:], forecasts[:-1]), 2))
-    ret.append(round(pinball_mean(0.05, original[model.order:], forecasts[:-1]), 2))
-    ret.append(round(pinball_mean(0.25, original[model.order:], forecasts[:-1]), 2))
-    ret.append(round(pinball_mean(0.75, original[model.order:], forecasts[:-1]), 2))
-    ret.append(round(pinball_mean(0.95, original[model.order:], forecasts[:-1]), 2))
+
+    if steps_ahead == 1:
+        forecasts = model.forecast_interval(data, **kwargs)
+        ret.append(round(sharpness(forecasts), 2))
+        ret.append(round(resolution(forecasts), 2))
+        ret.append(round(coverage(data[model.order:], forecasts[:-1]), 2))
+        ret.append(round(pinball_mean(0.05, data[model.order:], forecasts[:-1]), 2))
+        ret.append(round(pinball_mean(0.25, data[model.order:], forecasts[:-1]), 2))
+        ret.append(round(pinball_mean(0.75, data[model.order:], forecasts[:-1]), 2))
+        ret.append(round(pinball_mean(0.95, data[model.order:], forecasts[:-1]), 2))
+    else:
+        forecasts = []
+        for k in np.arange(model.order, len(data) - steps_ahead):
+            sample = data[k - model.order: k]
+            tmp = model.forecast_ahead_interval(sample, steps_ahead, **kwargs)
+            forecasts.append(tmp[-1])
+
+        start = model.order + steps_ahead
+        ret.append(round(sharpness(forecasts), 2))
+        ret.append(round(resolution(forecasts), 2))
+        ret.append(round(coverage(data[model.order:], forecasts), 2))
+        ret.append(round(pinball_mean(0.05, data[start:], forecasts), 2))
+        ret.append(round(pinball_mean(0.25, data[start:], forecasts), 2))
+        ret.append(round(pinball_mean(0.75, data[start:], forecasts), 2))
+        ret.append(round(pinball_mean(0.95, data[start:], forecasts), 2))
     return ret
 
 
-def get_distribution_statistics(original, model, **kwargs):
+def get_distribution_statistics(data, model, **kwargs):
+    steps_ahead = kwargs.get('steps_ahead', 1)
+
     ret = list()
-    _s1 = time.time()
-    densities1 = model.predict(original, **kwargs)
-    _e1 = time.time()
-    ret.append(round(crps(original, densities1), 3))
-    ret.append(round(_e1 - _s1, 3))
+
+    if steps_ahead == 1:
+        _s1 = time.time()
+        forecasts = model.forecast_distribution(data, **kwargs)
+        _e1 = time.time()
+        ret.append(round(crps(data, forecasts), 3))
+        ret.append(round(_e1 - _s1, 3))
+    else:
+        forecasts = []
+        _s1 = time.time()
+        for k in np.arange(model.order, len(data) - steps_ahead):
+            sample = data[k - model.order: k]
+            tmp = model.forecast_ahead_distribution(sample, steps_ahead, **kwargs)
+            forecasts.append(tmp[-1])
+        _e1 = time.time()
+
+        start = model.order + steps_ahead
+        ret.append(round(crps(data[start:], forecasts), 3))
+        ret.append(round(_e1 - _s1, 3))
     return ret
 
 
diff --git a/pyFTS/benchmarks/Util.py b/pyFTS/benchmarks/Util.py
index 1dc76f5..d5c71d1 100644
--- a/pyFTS/benchmarks/Util.py
+++ b/pyFTS/benchmarks/Util.py
@@ -15,7 +15,7 @@ from copy import deepcopy
 from pyFTS.common import Util
 
 
-def extract_measure(dataframe,measure,data_columns):
+def extract_measure(dataframe, measure, data_columns):
     if not dataframe.empty:
         df = dataframe[(dataframe.Measure == measure)][data_columns]
         tmp = df.to_dict(orient="records")[0]
@@ -92,12 +92,12 @@ def save_dataframe_point(experiments, file, objs, rmse, save, synthetic, smape,
                     mod.append(mfts.partitioner.name)
                     mod.append(mfts.partitioner.partitions)
                     mod.append(len(mfts))
-                    mod.append(steps)
-                    mod.append(method)
                 else:
                     mod.append('-')
                     mod.append('-')
                     mod.append('-')
+                mod.append(steps[k])
+                mod.append(method[k])
                 mod.append(np.round(np.nanmean(rmse[k]), 2))
                 mod.append(np.round(np.nanstd(rmse[k]), 2))
                 mod.append(np.round(np.nanmean(smape[k]), 2))
@@ -126,17 +126,18 @@ def save_dataframe_point(experiments, file, objs, rmse, save, synthetic, smape,
                     s = '-'
                     p = '-'
                     l = '-'
-                print([n, o, s, p, l, steps, method])
-                tmp = [n, o, s, p, l, steps, method, 'RMSE']
+                st = steps[k]
+                mt = method[k]
+                tmp = [n, o, s, p, l, st, mt, 'RMSE']
                 tmp.extend(rmse[k])
                 ret.append(deepcopy(tmp))
-                tmp = [n, o, s, p, l, steps, method, 'SMAPE']
+                tmp = [n, o, s, p, l, st, mt, 'SMAPE']
                 tmp.extend(smape[k])
                 ret.append(deepcopy(tmp))
-                tmp = [n, o, s, p, l, steps, method, 'U']
+                tmp = [n, o, s, p, l, st, mt, 'U']
                 tmp.extend(u[k])
                 ret.append(deepcopy(tmp))
-                tmp = [n, o, s, p, l, steps, method, 'TIME']
+                tmp = [n, o, s, p, l, st, mt, 'TIME']
                 tmp.extend(times[k])
                 ret.append(deepcopy(tmp))
             except Exception as ex:
@@ -154,13 +155,30 @@ def save_dataframe_point(experiments, file, objs, rmse, save, synthetic, smape,
         print(ret)
 
 
-def cast_dataframe_to_synthetic_point(infile, outfile, experiments):
-    columns = point_dataframe_analytic_columns(experiments)
+def cast_dataframe_to_synthetic(infile, outfile, experiments, type):
+    if type == 'point':
+        analytic_columns = point_dataframe_analytic_columns
+        synthetic_columns = point_dataframe_synthetic_columns
+        synthetize_measures = cast_dataframe_to_synthetic_point
+    elif type == 'interval':
+        analytic_columns = interval_dataframe_analytic_columns
+        synthetic_columns = interval_dataframe_synthetic_columns
+        synthetize_measures = cast_dataframe_to_synthetic_interval
+    elif type == 'distribution':
+        analytic_columns = probabilistic_dataframe_analytic_columns
+        synthetic_columns = probabilistic_dataframe_synthetic_columns
+        synthetize_measures = cast_dataframe_to_synthetic_probabilistic
+    else:
+        raise ValueError("Type parameter has an unknown value!")
+
+    columns = analytic_columns(experiments)
     dat = pd.read_csv(infile, sep=";", usecols=columns)
     models = dat.Model.unique()
     orders = dat.Order.unique()
     schemes = dat.Scheme.unique()
     partitions = dat.Partitions.unique()
+    steps = dat.Steps.unique()
+    methods = dat.Method.unique()
 
     data_columns = analytical_data_columns(experiments)
 
@@ -170,39 +188,48 @@ def cast_dataframe_to_synthetic_point(infile, outfile, experiments):
         for o in orders:
             for s in schemes:
                 for p in partitions:
-                    mod = []
-                    df = dat[(dat.Model == m) & (dat.Order == o) & (dat.Scheme == s) & (dat.Partitions == p)]
-                    if not df.empty:
-                        rmse = extract_measure(df, 'RMSE', data_columns)
-                        smape = extract_measure(df, 'SMAPE', data_columns)
-                        u = extract_measure(df, 'U', data_columns)
-                        times = extract_measure(df, 'TIME', data_columns)
-                        mod.append(m)
-                        mod.append(o)
-                        mod.append(s)
-                        mod.append(p)
-                        mod.append(extract_measure(df, 'RMSE', ['Size'])[0])
-                        mod.append(np.round(np.nanmean(rmse), 2))
-                        mod.append(np.round(np.nanstd(rmse), 2))
-                        mod.append(np.round(np.nanmean(smape), 2))
-                        mod.append(np.round(np.nanstd(smape), 2))
-                        mod.append(np.round(np.nanmean(u), 2))
-                        mod.append(np.round(np.nanstd(u), 2))
-                        mod.append(np.round(np.nanmean(times), 4))
-                        mod.append(np.round(np.nanstd(times), 4))
-                        ret.append(mod)
+                    for st in steps:
+                        for mt in methods:
+                            df = dat[(dat.Model == m) & (dat.Order == o) & (dat.Scheme == s) &
+                                     (dat.Partitions == p) & (dat.Steps == st) & (dat.Method == mt)]
+                            if not df.empty:
+                                mod = synthetize_measures(df, data_columns)
+                                mod.insert(0, m)
+                                mod.insert(1, o)
+                                mod.insert(2, s)
+                                mod.insert(3, p)
+                                mod.insert(4, df.iat[0,5])
+                                mod.insert(5, st)
+                                mod.insert(6, mt)
+                                ret.append(mod)
 
-    dat = pd.DataFrame(ret, columns=point_dataframe_synthetic_columns())
+    dat = pd.DataFrame(ret, columns=synthetic_columns())
     dat.to_csv(outfile, sep=";", index=False)
 
 
+def cast_dataframe_to_synthetic_point(df, data_columns):
+    ret = []
+    rmse = extract_measure(df, 'RMSE', data_columns)
+    smape = extract_measure(df, 'SMAPE', data_columns)
+    u = extract_measure(df, 'U', data_columns)
+    times = extract_measure(df, 'TIME', data_columns)
+    ret.append(np.round(np.nanmean(rmse), 2))
+    ret.append(np.round(np.nanstd(rmse), 2))
+    ret.append(np.round(np.nanmean(smape), 2))
+    ret.append(np.round(np.nanstd(smape), 2))
+    ret.append(np.round(np.nanmean(u), 2))
+    ret.append(np.round(np.nanstd(u), 2))
+    ret.append(np.round(np.nanmean(times), 4))
+    ret.append(np.round(np.nanstd(times), 4))
+
+    return ret
+
 
 def analytical_data_columns(experiments):
     data_columns = [str(k) for k in np.arange(0, experiments)]
     return data_columns
 
 
-
 def scale_params(data):
     vmin = np.nanmin(data)
     vlen = np.nanmax(data) - vmin
@@ -215,12 +242,10 @@ def scale(data, params):
     return ndata
 
 
-
 def stats(measure, data):
     print(measure, np.nanmean(data), np.nanstd(data))
 
 
-
 def unified_scaled_point(experiments, tam, save=False, file=None,
                          sort_columns=['UAVG', 'RMSEAVG', 'USTD', 'RMSESTD'],
                          sort_ascend=[1, 1, 1, 1],save_best=False,
@@ -330,7 +355,6 @@ def unified_scaled_point(experiments, tam, save=False, file=None,
     Util.show_and_save_image(fig, file, save)
 
 
-
 def 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,
@@ -419,14 +443,12 @@ def save_dataframe_interval(coverage, experiments, file, objs, resolution, save,
                 mod.append(mfts.partitioner.name)
                 mod.append(mfts.partitioner.partitions)
                 mod.append(l)
-                mod.append(steps)
-                mod.append(method)
             else:
                 mod.append('-')
                 mod.append('-')
                 mod.append('-')
-                mod.append(steps)
-                mod.append(method)
+            mod.append(steps[k])
+            mod.append(method[k])
             mod.append(round(np.nanmean(sharpness[k]), 2))
             mod.append(round(np.nanstd(sharpness[k]), 2))
             mod.append(round(np.nanmean(resolution[k]), 2))
@@ -461,29 +483,30 @@ def save_dataframe_interval(coverage, experiments, file, objs, resolution, save,
                     s = '-'
                     p = '-'
                     l = '-'
-
-                tmp = [n, o, s, p, l, steps, method, 'Sharpness']
+                st = steps[k]
+                mt = method[k]
+                tmp = [n, o, s, p, l, st, mt, 'Sharpness']
                 tmp.extend(sharpness[k])
                 ret.append(deepcopy(tmp))
-                tmp = [n, o, s, p, l, steps, method, 'Resolution']
+                tmp = [n, o, s, p, l, st, mt, 'Resolution']
                 tmp.extend(resolution[k])
                 ret.append(deepcopy(tmp))
-                tmp = [n, o, s, p, l, steps, method, 'Coverage']
+                tmp = [n, o, s, p, l, st, mt, 'Coverage']
                 tmp.extend(coverage[k])
                 ret.append(deepcopy(tmp))
-                tmp = [n, o, s, p, l, steps, method, 'TIME']
+                tmp = [n, o, s, p, l, st, mt, 'TIME']
                 tmp.extend(times[k])
                 ret.append(deepcopy(tmp))
-                tmp = [n, o, s, p, l, steps, method, 'Q05']
+                tmp = [n, o, s, p, l, st, mt, 'Q05']
                 tmp.extend(q05[k])
                 ret.append(deepcopy(tmp))
-                tmp = [n, o, s, p, l, steps, method, 'Q25']
+                tmp = [n, o, s, p, l, st, mt, 'Q25']
                 tmp.extend(q25[k])
                 ret.append(deepcopy(tmp))
-                tmp = [n, o, s, p, l, steps, method, 'Q75']
+                tmp = [n, o, s, p, l, st, mt, 'Q75']
                 tmp.extend(q75[k])
                 ret.append(deepcopy(tmp))
-                tmp = [n, o, s, p, l, steps, method, 'Q95']
+                tmp = [n, o, s, p, l, st, mt, 'Q95']
                 tmp.extend(q95[k])
                 ret.append(deepcopy(tmp))
             except Exception as ex:
@@ -515,57 +538,34 @@ def interval_dataframe_synthetic_columns():
     return columns
 
 
-def cast_dataframe_to_synthetic_interval(infile, outfile, experiments):
-    columns = interval_dataframe_analytic_columns(experiments)
-    dat = pd.read_csv(infile, sep=";", usecols=columns)
-    models = dat.Model.unique()
-    orders = dat.Order.unique()
-    schemes = dat.Scheme.unique()
-    partitions = dat.Partitions.unique()
-
-    data_columns = analytical_data_columns(experiments)
-
+def cast_dataframe_to_synthetic_interval(df, data_columns):
+    sharpness = extract_measure(df, 'Sharpness', data_columns)
+    resolution = extract_measure(df, 'Resolution', data_columns)
+    coverage = extract_measure(df, 'Coverage', data_columns)
+    times = extract_measure(df, 'TIME', data_columns)
+    q05 = extract_measure(df, 'Q05', data_columns)
+    q25 = extract_measure(df, 'Q25', data_columns)
+    q75 = extract_measure(df, 'Q75', data_columns)
+    q95 = extract_measure(df, 'Q95', data_columns)
     ret = []
+    ret.append(np.round(np.nanmean(sharpness), 2))
+    ret.append(np.round(np.nanstd(sharpness), 2))
+    ret.append(np.round(np.nanmean(resolution), 2))
+    ret.append(np.round(np.nanstd(resolution), 2))
+    ret.append(np.round(np.nanmean(coverage), 2))
+    ret.append(np.round(np.nanstd(coverage), 2))
+    ret.append(np.round(np.nanmean(times), 4))
+    ret.append(np.round(np.nanstd(times), 4))
+    ret.append(np.round(np.nanmean(q05), 4))
+    ret.append(np.round(np.nanstd(q05), 4))
+    ret.append(np.round(np.nanmean(q25), 4))
+    ret.append(np.round(np.nanstd(q25), 4))
+    ret.append(np.round(np.nanmean(q75), 4))
+    ret.append(np.round(np.nanstd(q75), 4))
+    ret.append(np.round(np.nanmean(q95), 4))
+    ret.append(np.round(np.nanstd(q95), 4))
+    return ret
 
-    for m in models:
-        for o in orders:
-            for s in schemes:
-                for p in partitions:
-                    mod = []
-                    df = dat[(dat.Model == m) & (dat.Order == o) & (dat.Scheme == s) & (dat.Partitions == p)]
-                    if not df.empty:
-                        sharpness = extract_measure(df, 'Sharpness', data_columns)
-                        resolution = extract_measure(df, 'Resolution', data_columns)
-                        coverage = extract_measure(df, 'Coverage', data_columns)
-                        times = extract_measure(df, 'TIME', data_columns)
-                        q05 = extract_measure(df, 'Q05', data_columns)
-                        q25 = extract_measure(df, 'Q25', data_columns)
-                        q75 = extract_measure(df, 'Q75', data_columns)
-                        q95 = extract_measure(df, 'Q95', data_columns)
-                        mod.append(m)
-                        mod.append(o)
-                        mod.append(s)
-                        mod.append(p)
-                        mod.append(np.round(np.nanmean(sharpness), 2))
-                        mod.append(np.round(np.nanstd(sharpness), 2))
-                        mod.append(np.round(np.nanmean(resolution), 2))
-                        mod.append(np.round(np.nanstd(resolution), 2))
-                        mod.append(np.round(np.nanmean(coverage), 2))
-                        mod.append(np.round(np.nanstd(coverage), 2))
-                        mod.append(np.round(np.nanmean(times), 4))
-                        mod.append(np.round(np.nanstd(times), 4))
-                        mod.append(np.round(np.nanmean(q05), 4))
-                        mod.append(np.round(np.nanstd(q05), 4))
-                        mod.append(np.round(np.nanmean(q25), 4))
-                        mod.append(np.round(np.nanstd(q25), 4))
-                        mod.append(np.round(np.nanmean(q75), 4))
-                        mod.append(np.round(np.nanstd(q75), 4))
-                        mod.append(np.round(np.nanmean(q95), 4))
-                        mod.append(np.round(np.nanstd(q95), 4))
-                        ret.append(mod)
-
-    dat = pd.DataFrame(ret, columns=interval_dataframe_synthetic_columns())
-    dat.to_csv(outfile, sep=";", index=False)
 
 
 
@@ -905,17 +905,14 @@ def save_dataframe_probabilistic(experiments, file, objs, crps, times, save, syn
                             mod.append(mfts.partitioner.name)
                             mod.append(mfts.partitioner.partitions)
                             mod.append(len(mfts))
-                            mod.append(steps)
-                            mod.append(method)
                         else:
                             mod.append('-')
                             mod.append('-')
                             mod.append('-')
-                            mod.append(steps)
-                            mod.append(method)
+                        mod.append(steps[k])
+                        mod.append(method[k])
                         mod.append(np.round(np.nanmean(crps[k]), 2))
                         mod.append(np.round(np.nanstd(crps[k]), 2))
-                        mod.append(l)
                         mod.append(np.round(np.nanmean(times[k]), 4))
                         mod.append(np.round(np.nanstd(times[k]), 4))
                         ret.append(mod)
@@ -940,10 +937,12 @@ def save_dataframe_probabilistic(experiments, file, objs, crps, times, save, syn
                     s = '-'
                     p = '-'
                     l = '-'
-                tmp = [n, o, s, p, l, steps, method, 'CRPS']
+                st = steps[k]
+                mt = method[k]
+                tmp = [n, o, s, p, l, st, mt, 'CRPS']
                 tmp.extend(crps[k])
                 ret.append(deepcopy(tmp))
-                tmp = [n, o, s, p, l, steps, method, 'TIME']
+                tmp = [n, o, s, p, l, st, mt, 'TIME']
                 tmp.extend(times[k])
                 ret.append(deepcopy(tmp))
             except Exception as ex:
@@ -974,40 +973,15 @@ def probabilistic_dataframe_synthetic_columns():
     return columns
 
 
-def cast_dataframe_to_synthetic_probabilistic(infile, outfile, experiments):
-    columns = probabilistic_dataframe_analytic_columns(experiments)
-    dat = pd.read_csv(infile, sep=";", usecols=columns)
-    models = dat.Model.unique()
-    orders = dat.Order.unique()
-    schemes = dat.Scheme.unique()
-    partitions = dat.Partitions.unique()
-
-    data_columns = analytical_data_columns(experiments)
-
+def cast_dataframe_to_synthetic_probabilistic(df, data_columns):
+    crps1 = extract_measure(df, 'CRPS', data_columns)
+    times1 = extract_measure(df, 'TIME', data_columns)
     ret = []
-
-    for m in models:
-        for o in orders:
-            for s in schemes:
-                for p in partitions:
-                    mod = []
-                    df = dat[(dat.Model == m) & (dat.Order == o) & (dat.Scheme == s) & (dat.Partitions == p)]
-                    if not df.empty:
-                        crps1 = extract_measure(df, 'CRPS', data_columns)
-                        times1 = extract_measure(df, 'TIME', data_columns)
-                        mod.append(m)
-                        mod.append(o)
-                        mod.append(s)
-                        mod.append(p)
-                        mod.append(np.round(np.nanmean(crps1), 2))
-                        mod.append(np.round(np.nanstd(crps1), 2))
-                        mod.append(np.round(np.nanmean(times1), 2))
-                        mod.append(np.round(np.nanstd(times1), 2))
-                        ret.append(mod)
-
-    dat = pd.DataFrame(ret, columns=probabilistic_dataframe_synthetic_columns())
-    dat.to_csv(outfile, sep=";", index=False)
-
+    ret.append(np.round(np.nanmean(crps1), 2))
+    ret.append(np.round(np.nanstd(crps1), 2))
+    ret.append(np.round(np.nanmean(times1), 2))
+    ret.append(np.round(np.nanstd(times1), 2))
+    return ret
 
 
 def unified_scaled_probabilistic(experiments, tam, save=False, file=None,
diff --git a/pyFTS/benchmarks/benchmarks.py b/pyFTS/benchmarks/benchmarks.py
index 409503f..3836756 100644
--- a/pyFTS/benchmarks/benchmarks.py
+++ b/pyFTS/benchmarks/benchmarks.py
@@ -81,6 +81,8 @@ def sliding_window_benchmarks(data, windowsize, train=0.8, **kwargs):
     partitioners_methods = __pop("partitioners_methods", [Grid.GridPartitioner], kwargs)
     partitions = __pop("partitions", [10], kwargs)
 
+    steps_ahead = __pop('steps_ahead', [1], kwargs)
+
     methods = __pop('methods', None, kwargs)
 
     models = __pop('models', None, kwargs)
@@ -178,27 +180,34 @@ def sliding_window_benchmarks(data, windowsize, train=0.8, **kwargs):
         else:
             partitioners_pool = partitioners_models
 
-        rng1 = partitioners_pool
-
+        rng1 = steps_ahead
         if progress:
-            rng1 = tqdm(partitioners_pool, desc="Partitioners")
+            rng1 = tqdm(steps_ahead, desc="Steps")
 
-        for partitioner in rng1:
-
-            rng2 = enumerate(pool,start=0)
+        for step in rng1:
+            rng2 = partitioners_pool
 
             if progress:
-                rng2 = enumerate(tqdm(pool, desc="Models"),start=0)
+                rng2 = tqdm(partitioners_pool, desc="Partitioners")
 
-            for _id, model in rng2:
+            for partitioner in rng2:
 
-                if not distributed:
-                    job = experiment_method(deepcopy(model), deepcopy(partitioner), train, test, **kwargs)
-                    jobs.append(job)
-                else:
-                    job = cluster.submit(deepcopy(model), deepcopy(partitioner), train, test, **kwargs)
-                    job.id = id  # associate an ID to identify jobs (if needed later)
-                    jobs.append(job)
+                rng3 = enumerate(pool,start=0)
+
+                if progress:
+                    rng3 = enumerate(tqdm(pool, desc="Models"),start=0)
+
+                for _id, model in rng3:
+
+                    kwargs['steps_ahead'] = step
+
+                    if not distributed:
+                        job = experiment_method(deepcopy(model), deepcopy(partitioner), train, test, **kwargs)
+                        jobs.append(job)
+                    else:
+                        job = cluster.submit(deepcopy(model), deepcopy(partitioner), train, test, **kwargs)
+                        job.id = id  # associate an ID to identify jobs (if needed later)
+                        jobs.append(job)
 
     if progress:
         progressbar.close()
@@ -303,6 +312,9 @@ def run_point(mfts, partitioner, train_data, test_data, window_key=None, **kwarg
         _key = mfts.shortname + " n = " + str(mfts.order) + " " + pttr + " q = " + str(partitioner.partitions)
         mfts.partitioner = partitioner
 
+    _key += str(steps_ahead)
+    _key += str(method) if method is not None else ""
+
     if transformation is not None:
         mfts.append_transformation(transformation)
 
@@ -363,6 +375,9 @@ def run_interval(mfts, partitioner, train_data, test_data, window_key=None, **kw
     if transformation is not None:
         mfts.append_transformation(transformation)
 
+    _key += str(steps_ahead)
+    _key += str(method) if method is not None else ""
+
     _start = time.time()
     mfts.fit(train_data, order=mfts.order, **kwargs)
     _end = time.time()
@@ -421,6 +436,9 @@ def run_probabilistic(mfts, partitioner, train_data, test_data, window_key=None,
         _key = mfts.shortname + " n = " + str(mfts.order) + " " + pttr + " q = " + str(partitioner.partitions)
         mfts.partitioner = partitioner
 
+    _key += str(steps_ahead)
+    _key += str(method) if method is not None else ""
+
     if transformation is not None:
         mfts.append_transformation(transformation)
 
@@ -478,8 +496,8 @@ def process_point_jobs(jobs, experiments, save=False, file=None, sintetic=False)
     smape = {}
     u = {}
     times = {}
-    steps = None
-    method = None
+    steps = {}
+    method = {}
 
     for job in jobs:
         _key = job['key']
@@ -489,14 +507,16 @@ def process_point_jobs(jobs, experiments, save=False, file=None, sintetic=False)
             smape[_key] = []
             u[_key] = []
             times[_key] = []
-            steps[_key] = job['steps']
-            method[_key] = job['method']
+            steps[_key] = []
+            method[_key] = []
+        steps[_key] = job['steps']
+        method[_key] = job['method']
         rmse[_key].append(job['rmse'])
         smape[_key].append(job['smape'])
         u[_key].append(job['u'])
         times[_key].append(job['time'])
 
-    return bUtil.save_dataframe_point(experiments, file, objs, rmse, save, sintetic, smape, times, u)
+    return bUtil.save_dataframe_point(experiments, file, objs, rmse, save, sintetic, smape, times, u, steps, method)
 
 
 def process_interval_jobs(jobs, experiments, save=False, file=None, sintetic=False):
@@ -509,6 +529,8 @@ def process_interval_jobs(jobs, experiments, save=False, file=None, sintetic=Fal
     q75 = {}
     q95 = {}
     times = {}
+    steps = {}
+    method = {}
 
     for job in jobs:
         _key = job['key']
@@ -522,6 +544,8 @@ def process_interval_jobs(jobs, experiments, save=False, file=None, sintetic=Fal
             q25[_key] = []
             q75[_key] = []
             q95[_key] = []
+            steps[_key] = []
+            method[_key] = []
 
         sharpness[_key].append(job['sharpness'])
         resolution[_key].append(job['resolution'])
@@ -531,16 +555,18 @@ def process_interval_jobs(jobs, experiments, save=False, file=None, sintetic=Fal
         q25[_key].append(job['Q25'])
         q75[_key].append(job['Q75'])
         q95[_key].append(job['Q95'])
-
-
+        steps[_key] = job['steps']
+        method[_key] = job['method']
     return bUtil.save_dataframe_interval(coverage, experiments, file, objs, resolution, save, sharpness, sintetic,
-                                         times, q05, q25, q75, q95)
+                                         times, q05, q25, q75, q95, steps, method)
 
 
 def process_probabilistic_jobs(jobs, experiments, save=False, file=None, sintetic=False):
     objs = {}
     crps = {}
     times = {}
+    steps = {}
+    method = {}
 
     for job in jobs:
         _key = job['key']
@@ -548,11 +574,15 @@ def process_probabilistic_jobs(jobs, experiments, save=False, file=None, sinteti
             objs[_key] = job['obj']
             crps[_key] = []
             times[_key] = []
+            steps[_key] = []
+            method[_key] = []
 
         crps[_key].append(job['CRPS'])
         times[_key].append(job['time'])
+        steps[_key] = job['steps']
+        method[_key] = job['method']
 
-    return bUtil.save_dataframe_probabilistic(experiments, file, objs, crps, times, save, sintetic)
+    return bUtil.save_dataframe_probabilistic(experiments, file, objs, crps, times, save, sintetic, steps, method)
 
 
 
diff --git a/pyFTS/common/fts.py b/pyFTS/common/fts.py
index 13aec21..31f56ea 100644
--- a/pyFTS/common/fts.py
+++ b/pyFTS/common/fts.py
@@ -89,19 +89,22 @@ class FTS(object):
 
             steps_ahead = kwargs.get("steps_ahead", None)
 
-            if type == 'point' and (steps_ahead == None or steps_ahead == 1):
-                ret = self.forecast(ndata, **kwargs)
-            elif type == 'point' and steps_ahead > 1:
-                ret = self.forecast_ahead(ndata, steps_ahead, **kwargs)
-            elif type == 'interval' and (steps_ahead == None or steps_ahead == 1):
-                ret = self.forecast_interval(ndata, **kwargs)
-            elif type == 'interval' and steps_ahead > 1:
-                ret = self.forecast_ahead_interval(ndata, steps_ahead, **kwargs)
-            elif type == 'distribution' and (steps_ahead == None or steps_ahead == 1):
-                ret = self.forecast_distribution(ndata, **kwargs)
-            elif type == 'distribution' and steps_ahead > 1:
-                ret = self.forecast_ahead_distribution(ndata, steps_ahead, **kwargs)
-            else:
+            if steps_ahead == None or steps_ahead == 1:
+                if type == 'point':
+                    ret = self.forecast(ndata, **kwargs)
+                elif type == 'interval':
+                    ret = self.forecast_interval(ndata, **kwargs)
+                elif type == 'distribution':
+                    ret = self.forecast_distribution(ndata, **kwargs)
+            elif steps_ahead > 1:
+                if type == 'point':
+                    ret = self.forecast_ahead(ndata, steps_ahead, **kwargs)
+                elif type == 'interval':
+                    ret = self.forecast_ahead_interval(ndata, steps_ahead, **kwargs)
+                elif type == 'distribution':
+                    ret = self.forecast_ahead_distribution(ndata, steps_ahead, **kwargs)
+
+            if not ['point', 'interval', 'distribution'].__contains__(type):
                 raise ValueError('The argument \'type\' has an unknown value.')
 
         else:
diff --git a/pyFTS/models/pwfts.py b/pyFTS/models/pwfts.py
index 3980122..3fd3a09 100644
--- a/pyFTS/models/pwfts.py
+++ b/pyFTS/models/pwfts.py
@@ -372,6 +372,9 @@ class ProbabilisticWeightedFTS(ifts.IntervalFTS):
 
             flrgs = self.generate_lhs_flrg(sample)
 
+            if 'type' in kwargs:
+                kwargs.pop('type')
+
             dist = ProbabilityDistribution.ProbabilityDistribution(smooth, uod=uod, bins=_bins, **kwargs)
 
             for bin in _bins:
@@ -409,7 +412,7 @@ class ProbabilisticWeightedFTS(ifts.IntervalFTS):
                 ret.append(ret[-1])
             else:
                 mp = self.forecast([ret[x] for x in np.arange(k - self.order, k)], **kwargs)
-                ret.append(mp)
+                ret.append(mp[0])
 
         return ret[self.order:]
 
@@ -427,13 +430,13 @@ class ProbabilisticWeightedFTS(ifts.IntervalFTS):
 
         start = kwargs.get('start', self.order)
 
-        sample = data[start - (self.order - 1): start + 1]
+        sample = data[start - self.order: start]
 
         ret = [[k, k] for k in sample]
 
         for k in np.arange(self.order, steps+self.order):
 
-            if self.__check_interval_bounds(ret[-1]):
+            if len(ret) > 0 and self.__check_interval_bounds(ret[-1]):
                 ret.append(ret[-1])
             else:
                 lower = self.forecast_interval([ret[x][0] for x in np.arange(k - self.order, k)], **kwargs)
@@ -460,9 +463,11 @@ class ProbabilisticWeightedFTS(ifts.IntervalFTS):
 
         start = kwargs.get('start', self.order)
 
-        sample = ndata[start - (self.order - 1): start + 1]
+        sample = ndata[start - self.order: start]
 
         for dat in sample:
+            if 'type' in kwargs:
+                kwargs.pop('type')
             tmp = ProbabilityDistribution.ProbabilityDistribution(smooth, uod=uod, bins=_bins, **kwargs)
             tmp.set(dat, 1.0)
             ret.append(tmp)
diff --git a/pyFTS/probabilistic/ProbabilityDistribution.py b/pyFTS/probabilistic/ProbabilityDistribution.py
index ea50ab9..ce9b78d 100644
--- a/pyFTS/probabilistic/ProbabilityDistribution.py
+++ b/pyFTS/probabilistic/ProbabilityDistribution.py
@@ -11,7 +11,7 @@ class ProbabilityDistribution(object):
     If type is histogram, the PDF is discrete
     If type is KDE the PDF is continuous
     """
-    def __init__(self,type = "KDE", **kwargs):
+    def __init__(self, type = "KDE", **kwargs):
         self.uod = kwargs.get("uod", None)
 
         self.type = type
diff --git a/pyFTS/tests/general.py b/pyFTS/tests/general.py
index 0d2f916..38b03f9 100644
--- a/pyFTS/tests/general.py
+++ b/pyFTS/tests/general.py
@@ -19,14 +19,15 @@ from pyFTS.benchmarks import benchmarks as bchmk
 
 from pyFTS.models import pwfts
 
-'''
+#'''
 bchmk.sliding_window_benchmarks(dataset, 1000, train=0.8, inc=0.2, methods=[pwfts.ProbabilisticWeightedFTS],
                                 benchmark_models=False, orders=[1], partitions=[10], #np.arange(10,100,2),
-                                progress=False, type='distribution',
-                                distributed=False, nodes=['192.168.0.106', '192.168.0.105', '192.168.0.110'],
-                                save=True, file="pwfts_taiex_interval.csv")
-'''
+                                progress=False, type='distribution', steps_ahead=[1,4,7,10],
+                                #distributed=False, nodes=['192.168.0.106', '192.168.0.105', '192.168.0.110'],
+                                save=True, file="pwfts_taiex_distribution.csv")
+#'''
 
+'''
 train_split = 2000
 test_length = 200
 
@@ -55,7 +56,7 @@ tmp = pfts1_taiex.predict(dataset[train_split:train_split+200], type='point',
 
 
 print(tmp)
-
+'''
 
 '''
 tmp = pfts1_taiex.predict(dataset[train_split:train_split+200], type='diPedro Pazzini