import pyaf.Bench.TS_datasets as tsds
import pyaf.tests.artificial.process_artificial_dataset as art
dataset = tsds.generate_random_TS(N=128,
FREQ='D',
seed=0,
trendtype="constant",
cycle_length=5,
transform="log",
sigma=0.0,
exog_count=20,
ar_order=12)
art.process_dataset(dataset)
lExogenousDataFrame['Date_dayofweek'] = lDate.dt.dayofweek
lExogenousDataFrame['Date_day'] = lDate.dt.day
lExogenousDataFrame['Date_dayofyear'] = lDate.dt.dayofyear
lExogenousDataFrame['Date_month'] = lDate.dt.month
lExogenousDataFrame['Date_week'] = lDate.dt.week
# a column in the exog data can be of any type
lExogenousDataFrame['Date_day_name'] = lDate.dt.day_name()
lExogenousDataFrame['Date_month_name'] = lDate.dt.month_name()
lExogenousVariables = [
col for col in lExogenousDataFrame.columns if col.startswith('Date_')
]
lExogenousData = (lExogenousDataFrame, lExogenousVariables)
return lExogenousData
b1 = tsds.load_AU_hierarchical_dataset()
df = b1.mPastData
lEngine = hautof.cHierarchicalForecastEngine()
lEngine.mOptions.mHierarchicalCombinationMethod = ["BU", 'TD', 'MO', 'OC']
lEngine.mOptions.mNbCores = 16
lEngine
H = b1.mHorizon
# lEngine.mOptions.enable_slow_mode();
# lEngine.mOptions.mDebugPerformance = True;
lExogenousData = create_exog_data(b1)
lEngine.train(df,
b1.mTimeVar,
import pyaf.Bench.TS_datasets as tsds
import pyaf.tests.artificial.process_artificial_dataset as art
dataset = tsds.generate_random_TS(N=1024,
FREQ='D',
seed=0,
trendtype="poly",
cycle_length=30,
transform="log",
sigma=0.0,
exog_count=20,
ar_order=0)
art.process_dataset(dataset)
from pyaf.Bench import MComp as mcomp
#tester1 = mcomp.cMComp_Tester(tsds.load_M1_comp());
#tester1.testSignal('')
#tester1.testAllSignals()
#tester2 = mcomp.cMComp_Tester(tsds.load_M2_comp());
#tester1.testSignal('')
#tester2.testAllSignals()
#tester3 = mcomp.cMComp_Tester(tsds.load_M3_Y_comp());
#tester1.testSignal('')
#tester3.testAllSignals()
#tester4 = mcomp.cMComp_Tester(tsds.load_M3_Q_comp());
#tester1.testSignal('')
#tester4.testAllSignals()
#tester5 = mcomp.cMComp_Tester(tsds.load_M3_M_comp());
#tester1.testSignal('')
#tester5.testAllSignals()
#tester6 = mcomp.cMComp_Tester(tsds.load_M3_Other_comp());
#tester1.testSignal('')
#tester6.testAllSignals()
tester7 = mcomp.cMComp_Tester(tsds.load_M4_comp() , "M4_COMP");
#tester7.testSignal('FIN1')
tester7.testAllSignals()
import pandas as pd
import numpy as np
import pyaf.ForecastEngine as autof
import pyaf.Bench.TS_datasets as tsds
#get_ipython().magic('matplotlib inline')
b1 = tsds.generate_random_TS(N=320,
FREQ='D',
seed=0,
trendtype="constant",
cycle_length=0,
transform="",
sigma=0.1,
ar_order=12)
df = b1.mPastData
#df.tail(10)
#df[:-10].tail()
#df[:-10:-1]
#df.describe()
lEngine = autof.cForecastEngine()
lEngine
lEngine.mOptions.set_active_transformations([])
lEngine.mOptions.set_active_trends([])
lEngine.mOptions.set_active_periodics([])
lEngine.mOptions.set_active_autoregressions(['AR', 'ARX'])
H = b1.mHorizon
import pyaf.Bench.TS_datasets as tsds
import pyaf.tests.artificial.process_artificial_dataset as art
dataset = tsds.generate_random_TS(N=32,
FREQ='D',
seed=0,
trendtype="constant",
cycle_length=30,
transform="exp",
sigma=0.0,
exog_count=100,
ar_order=12)
art.process_dataset(dataset)
import pyaf.Bench.TS_datasets as tsds
import pyaf.Bench.YahooStocks as ys
tester7 = ys.cYahoo_Tester(tsds.load_yahoo_stock_prices("cac40"),
"YAHOO_STOCKS")
#tester7.testAllSignals(12);
tester7.testSignals("ML.PA", 12)
# tester7.run_multiprocessed(18);
import pandas as pd
import numpy as np
import pyaf.ForecastEngine as autof
import pyaf.Bench.TS_datasets as tsds
import pyaf.CodeGen.TS_CodeGenerator as tscodegen
#get_ipython().magic('matplotlib inline')
b1 = tsds.generate_random_TS(N=3200,
FREQ='D',
seed=0,
trendtype="constant",
cycle_length=12,
transform="",
sigma=0.0,
exog_count=200)
df = b1.mPastData
H = b1.mHorizon
N = df.shape[0]
for n in [N // 8, N // 4, N // 2, N]:
df1 = df.head(n).copy()
lEngine = autof.cForecastEngine()
lEngine.mOptions.mEnableSeasonals = False
# lEngine.mOptions.mDebugCycles = True;
lEngine
lEngine.train(df1, b1.mTimeVar, b1.mSignalVar, H, None)
lEngine.getModelInfo()
import pyaf.Bench.TS_datasets as tsds
import pyaf.Bench.GenericBenchmark as tBench
import warnings
#%matplotlib inline
tester1 = tBench.cGeneric_Tester(tsds.load_MWH_datsets(), "MWH_BENCH")
with warnings.catch_warnings():
warnings.simplefilter("error")
tester1.testSignals('plastics')
# tester1.testAllSignals()
# tester1.run_multiprocessed();
import pandas as pd
import numpy as np
import pyaf.ForecastEngine as autof
import pyaf.Bench.TS_datasets as tsds
b1 = tsds.load_airline_passengers()
df = b1.mPastData
df.head()
lEngine = autof.cForecastEngine()
lEngine
H = b1.mHorizon;
# lEngine.mOptions.enable_slow_mode();
# lEngine.mOptions.mDebugPerformance = True;
lEngine.mOptions.mParallelMode = True;
# lEngine.mOptions.set_active_autoregressions(['XGB']);
lEngine.train(df , b1.mTimeVar , b1.mSignalVar, H);
lEngine.getModelInfo();
print(lEngine.mSignalDecomposition.mTrPerfDetails.head());
lEngine.mSignalDecomposition.mBestModel.mTimeInfo.mResolution
lEngine.standardPlots(name = "outputs/my_airline_passengers")
dfapp_in = df.copy();
import pandas as pd import numpy as np import pyaf.ForecastEngine as autof import pyaf.Bench.TS_datasets as tsds b1 = tsds.load_ozone_exogenous_categorical() df = b1.mPastData print(b1.mExogenousDataFrame.Exog2.cat.categories) print(b1.mExogenousDataFrame.Exog3.cat.categories) print(b1.mExogenousDataFrame.Exog4.cat.categories) lEngine = autof.cForecastEngine() lEngine.mOptions.mDebug = True lEngine.mOptions.mDebugProfile = True lEngine.mOptions.disable_all_periodics() lEngine.mOptions.set_active_autoregressions(['ARX']) lExogenousData = (b1.mExogenousDataFrame, b1.mExogenousVariables) H = 12 lEngine.train(df, 'Time', b1.mSignalVar, H, lExogenousData) lEngine lEngine.getModelInfo() lEngine.mSignalDecomposition.mBestModel.mTimeInfo.mResolution lEngine.standardPlots(name="outputs/my_categorical_arx_ozone") dfapp_in = df.copy() dfapp_in.tail()
import pandas as pd
import numpy as np
import pyaf.Bench.MComp as mcomp
import pyaf.Bench.TS_datasets as tsds
import warnings
tester2 = mcomp.cMComp_Tester(tsds.load_M2_comp(), "M2_COMP")
with warnings.catch_warnings():
warnings.simplefilter("error")
# tester1.testSignal('')
# tester2.testAllSignals()
tester2.run_multiprocessed()
import numpy as np
import pyaf.ForecastEngine as autof
import pyaf.Bench.TS_datasets as tsds
#get_ipython().magic('matplotlib inline')
lValues = [2]
# lValues = lValues + [ k for k in range(24, 128, 8)];
for nbrows in range(1000, 32000, 1000):
cyc = lValues[0]
print("TEST_CYCLES_START", nbrows, cyc)
b1 = tsds.generate_random_TS(N=nbrows,
FREQ='H',
seed=0,
trendtype="constant",
cycle_length=cyc,
transform="",
sigma=0.1,
exog_count=0)
df = b1.mPastData
# df.tail(10)
# df[:-10].tail()
# df[:-10:-1]
# df.describe()
lEngine = autof.cForecastEngine()
lEngine.mOptions.mCycleLengths = [k for k in range(2, 128)]
lEngine
H = cyc * 2
import pandas as pd
import numpy as np
import pyaf.ForecastEngine as autof
import pyaf.Bench.TS_datasets as tsds
#get_ipython().magic('matplotlib inline')
b1 = tsds.load_ozone()
df = b1.mPastData
#df.tail(10)
#df[:-10].tail()
#df[:-10:-1]
#df.describe()
lEngine = autof.cForecastEngine()
lEngine
H = b1.mHorizon
# lEngine.mOptions.enable_slow_mode();
# lEngine.mOptions.mDebugPerformance = True;
lEngine.train(df, b1.mTimeVar, b1.mSignalVar, H)
lEngine.getModelInfo()
print(lEngine.mSignalDecomposition.mTrPerfDetails.head())
lEngine.mSignalDecomposition.mBestModel.mTimeInfo.mResolution
lEngine.standardPlots("outputs/my_ozone")
dfapp_in = df.copy()
dfapp_in.tail()
import pyaf.Bench.TS_datasets as tsds
import pyaf.tests.artificial.process_artificial_dataset as art
dataset = tsds.generate_random_TS(N=32,
FREQ='D',
seed=0,
trendtype="linear",
cycle_length=5,
transform="pow3",
sigma=0.0,
exog_count=100,
ar_order=12)
art.process_dataset(dataset)
import pandas as pd
import numpy as np
import pyaf.Bench.TS_datasets as tsds
import pyaf.Bench.YahooStocks as ys
symbol_lists = tsds.get_yahoo_symbol_lists();
y_keys = sorted(symbol_lists.keys())
print(y_keys)
k = "nysecomp"
tester = ys.cYahoo_Tester(tsds.load_yahoo_stock_prices(k) , "YAHOO_STOCKS_" + k);
tester.testSignals('VRS');
import pandas as pd
import numpy as np
import pyaf.ForecastEngine as autof
import pyaf.Bench.TS_datasets as tsds
# get_ipython().magic('matplotlib inline')
# %load_ext memory_profiler
lValues = [ 64 ];
for cyc in lValues:
print("TEST_CYCLES_START", cyc)
b1 = tsds.generate_random_TS(N = 4800 , FREQ = 'D', seed = 0, trendtype = "linear", cycle_length = cyc, transform = "", sigma = 0.0, exog_count = 100);
df = b1.mPastData
# df.tail(10)
# df[:-10].tail()
# df[:-10:-1]
# df.describe()
lEngine = autof.cForecastEngine()
lEngine.mOptions.mCycleLengths = [ k for k in range(2,128) ];
lEngine
H = b1.mHorizon;
lEngine.train(df , b1.mTimeVar , b1.mSignalVar, H);
lEngine.getModelInfo();
lEngine.mSignalDecomposition.mBestModel.mTimeInfo.mResolution
import pyaf.Bench.TS_datasets as tsds import pyaf.tests.artificial.process_artificial_dataset as art dataset = tsds.generate_random_TS(N = 1024 , FREQ = 'D', seed = 0, trendtype = "constant", cycle_length = 7, transform = "sqr", sigma = 0.0, exog_count = 0, ar_order = 0); art.process_dataset(dataset);
import pandas as pd
import numpy as np
import pyaf.ForecastEngine as autof
import pyaf.Bench.TS_datasets as tsds
import warnings
with warnings.catch_warnings():
# warnings.simplefilter("error")
b1 = tsds.generate_random_TS(N = 160 , FREQ = 'D', seed = 0, trendtype = "constant", cycle_length = 12, transform = "None", sigma = 0.10, exog_count = 1280);
df = b1.mPastData
df.info()
# df.to_csv("outputs/rand_exogenous.csv")
H = b1.mHorizon[b1.mSignalVar];
N = df.shape[0];
for n in [N]:
for nbex in [1280]:
df1 = df.head(n).copy();
lEngine = autof.cForecastEngine()
lEngine.mOptions.mMaxFeatureForAutoreg = 10000;
lEngine.mOptions.mParallelMode = False;
# lEngine.mOptions.mEnableSeasonals = False;
# lEngine.mOptions.mDebugCycles = True;
# lEngine.mOptions.mDebugProfile = True;
lEngine
lExogenousData = (b1.mExogenousDataFrame , b1.mExogenousVariables[0:nbex])
lEngine.train(df1 , b1.mTimeVar , b1.mSignalVar, H, lExogenousData);
import pyaf.Bench.TS_datasets as tsds
import pyaf.tests.artificial.process_artificial_dataset as art
dataset = tsds.generate_random_TS(N=128,
FREQ='D',
seed=0,
trendtype="linear",
cycle_length=7,
transform="inv",
sigma=0.0,
exog_count=20,
ar_order=0)
art.process_dataset(dataset)
import pandas as pd
import numpy as np
import pyaf.HierarchicalForecastEngine as hautof
import pyaf.Bench.TS_datasets as tsds
import datetime
#get_ipython().magic('matplotlib inline')
b1 = tsds.load_AU_infant_grouped_dataset();
# reduce the number of possible values of State.
b1.mHierarchy['Groups']['State'] = ["NSW","VIC"];
df = b1.mPastData;
lEngine = hautof.cHierarchicalForecastEngine()
lEngine.mOptions.mHierarchicalCombinationMethod = ["BU" , 'TD' , 'MO' , 'OC'];
lEngine
H = b1.mHorizon;
# lEngine.mOptions.enable_slow_mode();
# lEngine.mOptions.mDebugPerformance = True;
lEngine.mOptions.set_active_autoregressions([]);
lEngine.train(df , b1.mTimeVar , b1.mSignalVar, H, b1.mHierarchy, None);
lEngine.getModelInfo();
#lEngine.standrdPlots("outputs/AU_infant_");
dfapp_in = df.copy();
import pyaf.Bench.TS_datasets as tsds
import pyaf.tests.artificial.process_artificial_dataset as art
dataset = tsds.generate_random_TS(N=1024,
FREQ='D',
seed=0,
trendtype="linear",
cycle_length=12,
transform="cumsum",
sigma=0.0,
exog_count=0,
ar_order=12)
art.process_dataset(dataset)
import pyaf.ForecastEngine as autof import pyaf.Bench.TS_datasets as tsds b1 = tsds.generate_random_TS(N = 4000 , FREQ = 'D', seed = 0, trendtype = "constant", cycle_length = 24, transform = "None", sigma = 0.1, exog_count = 0); df = b1.mPastData lEngine = autof.cForecastEngine() lEngine H = 12; lEngine.mOptions.mFilterSeasonals = False; lEngine.mOptions.mParallelMode = False; lEngine.mOptions.mDebugPerformance = True; lEngine.train(df , b1.mTimeVar , b1.mSignalVar, H); lEngine.getModelInfo(); print(lEngine.mSignalDecomposition.mTrPerfDetails.head());
import pyaf.Bench.TS_datasets as tsds
import pyaf.tests.artificial.process_artificial_dataset as art
dataset = tsds.generate_random_TS(N=128,
FREQ='D',
seed=0,
trendtype="poly",
cycle_length=5,
transform="sqrt",
sigma=0.0,
exog_count=100,
ar_order=0)
art.process_dataset(dataset)
dfapp_out.tail(2 * H)
print("Forecast Columns ", dfapp_out.columns)
Forecast_DF = dfapp_out[[
idataset.mTimeVar, lSignalVar, lSignalVar + '_Forecast'
]]
print(Forecast_DF.info())
print("Forecasts\n",
Forecast_DF.tail(H).values)
print("\n\n<ModelInfo>")
print(lEngine.to_json())
print("</ModelInfo>\n\n")
print("\n\n<Forecast>")
print(Forecast_DF.tail(2 * H).to_json(date_format='iso'))
print("</Forecast>\n\n")
with warnings.catch_warnings():
warnings.simplefilter("error")
dataset = tsds.generate_random_TS(N=40,
FREQ='D',
seed=0,
trendtype="linear",
cycle_length=4,
transform="exp",
sigma=2.0,
exog_count=0)
process_dataset(dataset)
