Add Croston Method #97

Added two more tests

tests/croston/test_croston_fpp2_counts_example_optimize.py

@@ -0,0 +1,50 @@
+import pandas as pd
+
+def create_dataset():
+    lCounts = "0 2 0 1 0 11 0 0 0 0 2 0 6 3 0 0 0 0 0 7 0 0 0 0 0 0 0 3 1 0 0 1 0 1 0 0".split()
+    lCounts = [float(c) for c in lCounts]
+    N = len(lCounts)
+    lDates = pd.date_range(start="2000-01-01", periods=N, freq='m')
+
+    df = pd.DataFrame({"Date" : lDates, "Signal" : lCounts})
+    return df
+
+def create_model(croston_type):
+    df_train = create_dataset()
+    # print(df_train.head(N))
+
+    import pyaf.ForecastEngine as autof
+    lEngine = autof.cForecastEngine()
+
+    lEngine.mOptions.set_active_trends(['ConstantTrend', 'LinearTrend'])
+    lEngine.mOptions.set_active_periodics(['None'])
+    lEngine.mOptions.set_active_transformations(['None'])
+    lEngine.mOptions.set_active_autoregressions(['CROSTON'])
+    lEngine.mOptions.mModelSelection_Criterion = "L2";
+    lEngine.mOptions.mCrostonOptions.mMethod = croston_type
+    lEngine.mOptions.mCrostonOptions.mZeroRate = 0.0
+    lEngine.mOptions.mCrostonOptions.mAlpha = None
+
+    # get the best time series model for predicting one week
+    lEngine.train(iInputDS = df_train, iTime = 'Date', iSignal = 'Signal', iHorizon = 7);
+
+    lEngine.getModelInfo()
+
+    lName = "outputs/fpp2_croston_optimize_" + str(croston_type) + "_"
+    lEngine.standardPlots(lName);
+
+    # predict one week
+    df_forecast = lEngine.forecast(iInputDS = df_train, iHorizon = 7)
+    # list the columns of the forecast dataset
+    print(df_forecast.columns) #
+
+    cols = ['Date', 'Signal', '_Signal',
+            '_Signal_TransformedForecast', 'Signal_Forecast']
+    # print the real forecasts
+    print(df_forecast[cols].tail(12))
+
+    print(df_forecast['Signal'].describe())
+    print(df_forecast['Signal_Forecast'].describe())
+
+
+create_model(None)

tests/croston/test_croston_fpp2_counts_example_slow_mode.py

@@ -0,0 +1,52 @@
+import pandas as pd
+
+def create_dataset():
+    lCounts = "0 2 0 1 0 11 0 0 0 0 2 0 6 3 0 0 0 0 0 7 0 0 0 0 0 0 0 3 1 0 0 1 0 1 0 0".split()
+    lCounts = [float(c) for c in lCounts]
+    N = len(lCounts)
+    lDates = pd.date_range(start="2000-01-01", periods=N, freq='m')
+
+    df = pd.DataFrame({"Date" : lDates, "Signal" : lCounts})
+    return df
+
+def create_model(croston_type):
+    df_train = create_dataset()
+    # print(df_train.head(N))
+
+    import pyaf.ForecastEngine as autof
+    lEngine = autof.cForecastEngine()
+
+    lSlowMode = False
+    if(lSlowMode):
+        lEngine.mOptions.set_active_trends(['ConstantTrend', 'LinearTrend'])
+        lEngine.mOptions.set_active_periodics(['None'])
+        lEngine.mOptions.set_active_transformations(['None'])
+        lEngine.mOptions.set_active_autoregressions(['CROSTON'])
+
+    lEngine.mOptions.mModelSelection_Criterion = "L2";
+    lEngine.mOptions.mCrostonOptions.mMethod = croston_type
+    lEngine.mOptions.mCrostonOptions.mZeroRate = 0.0
+
+    # get the best time series model for predicting one week
+    lEngine.train(iInputDS = df_train, iTime = 'Date', iSignal = 'Signal', iHorizon = 7);
+
+    lEngine.getModelInfo()
+
+    lName = "outputs/fpp2_slow_mode_" + str(croston_type) + "_"
+    lEngine.standardPlots(lName);
+
+    # predict one week
+    df_forecast = lEngine.forecast(iInputDS = df_train, iHorizon = 7)
+    # list the columns of the forecast dataset
+    print(df_forecast.columns) #
+
+    cols = ['Date', 'Signal', '_Signal',
+            '_Signal_TransformedForecast', 'Signal_Forecast']
+    # print the real forecasts
+    print(df_forecast[cols].tail(12))
+
+    print(df_forecast['Signal'].describe())
+    print(df_forecast['Signal_Forecast'].describe())
+
+
+create_model(None)