Custom predictors for recursive multi-step forecasting¶

It may be interesting to include additional features of the time series besides the lags. For example, the moving average of the n last values ​​can be used to capture the trend of the series.

The ForecasterAutoregCustom class is very similar to the ForecasterAutoreg class described in the previous section, but the user defines the function used to create the predictors.

Libraries¶

In [1]:

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from skforecast.ForecasterAutoregCustom import ForecasterAutoregCustom
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_squared_error

import numpy as np import pandas as pd import matplotlib.pyplot as plt from skforecast.ForecasterAutoregCustom import ForecasterAutoregCustom from sklearn.ensemble import RandomForestRegressor from sklearn.metrics import mean_squared_error

Data¶

In [2]:

# Download data
# ==============================================================================
url = ('https://raw.githubusercontent.com/JoaquinAmatRodrigo/skforecast/master/data/h2o.csv')
data = pd.read_csv(url, sep=',', header=0, names=['y', 'datetime'])

# Data preprocessing
# ==============================================================================
data['datetime'] = pd.to_datetime(data['datetime'], format='%Y/%m/%d')
data = data.set_index('datetime')
data = data.asfreq('MS')
data = data['y']
data = data.sort_index()

# Split train-test
# ==============================================================================
steps = 36
data_train = data[:-steps]
data_test  = data[-steps:]

# Plot
# ==============================================================================
fig, ax=plt.subplots(figsize=(9, 4))
data_train.plot(ax=ax, label='train')
data_test.plot(ax=ax, label='test')
ax.legend();

# Download data # ============================================================================== url = ('https://raw.githubusercontent.com/JoaquinAmatRodrigo/skforecast/master/data/h2o.csv') data = pd.read_csv(url, sep=',', header=0, names=['y', 'datetime']) # Data preprocessing # ============================================================================== data['datetime'] = pd.to_datetime(data['datetime'], format='%Y/%m/%d') data = data.set_index('datetime') data = data.asfreq('MS') data = data['y'] data = data.sort_index() # Split train-test # ============================================================================== steps = 36 data_train = data[:-steps] data_test = data[-steps:] # Plot # ============================================================================== fig, ax=plt.subplots(figsize=(9, 4)) data_train.plot(ax=ax, label='train') data_test.plot(ax=ax, label='test') ax.legend();

Train forecaster¶

In [3]:

# Custom function to create predictors
# ==============================================================================
def create_predictors(y):
    """
    Create first 10 lags of a time series.
    Calculate moving average with window 20.
    """

    lags = y[-1:-11:-1]
    mean = np.mean(y[-20:])
    predictors = np.hstack([lags, mean])

    return predictors

# Custom function to create predictors # ============================================================================== def create_predictors(y): """ Create first 10 lags of a time series. Calculate moving average with window 20. """ lags = y[-1:-11:-1] mean = np.mean(y[-20:]) predictors = np.hstack([lags, mean]) return predictors

In [4]:

# Create and fit forecaster
# ==============================================================================
forecaster = ForecasterAutoregCustom(
                 regressor      = RandomForestRegressor(random_state=123),
                 fun_predictors = create_predictors,
                 window_size    = 20
             )

forecaster.fit(y=data_train)
forecaster

# Create and fit forecaster # ============================================================================== forecaster = ForecasterAutoregCustom( regressor = RandomForestRegressor(random_state=123), fun_predictors = create_predictors, window_size = 20 ) forecaster.fit(y=data_train) forecaster

Out[4]:

======================= 
ForecasterAutoregCustom 
======================= 
Regressor: RandomForestRegressor(random_state=123) 
Predictors created with function: create_predictors 
Window size: 20 
Transformer for y: None 
Transformer for exog: None 
Included exogenous: False 
Type of exogenous variable: None 
Exogenous variables names: None 
Training range: [Timestamp('1991-07-01 00:00:00'), Timestamp('2005-06-01 00:00:00')] 
Training index type: DatetimeIndex 
Training index frequency: MS 
Regressor parameters: {'bootstrap': True, 'ccp_alpha': 0.0, 'criterion': 'squared_error', 'max_depth': None, 'max_features': 1.0, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 123, 'verbose': 0, 'warm_start': False} 
Creation date: 2022-09-24 08:26:57 
Last fit date: 2022-09-24 08:26:57 
Skforecast version: 0.5.0 
Python version: 3.9.13 

Prediction¶

In [5]:

# Predict
# ==============================================================================
steps = 36
predictions = forecaster.predict(steps=steps)
predictions.head(3)

# Predict # ============================================================================== steps = 36 predictions = forecaster.predict(steps=steps) predictions.head(3)

Out[5]:

2005-07-01    0.926598
2005-08-01    0.948202
2005-09-01    1.020947
Freq: MS, Name: pred, dtype: float64

In [6]:

# Plot predictions
# ==============================================================================
fig, ax=plt.subplots(figsize=(9, 4))
data_train.plot(ax=ax, label='train')
data_test.plot(ax=ax, label='test')
predictions.plot(ax=ax, label='predictions')
ax.legend();

# Plot predictions # ============================================================================== fig, ax=plt.subplots(figsize=(9, 4)) data_train.plot(ax=ax, label='train') data_test.plot(ax=ax, label='test') predictions.plot(ax=ax, label='predictions') ax.legend();

In [7]:

# Prediction error
# ==============================================================================
error_mse = mean_squared_error(
                y_true = data_test,
                y_pred = predictions
            )
print(f"Test error (mse): {error_mse}")

# Prediction error # ============================================================================== error_mse = mean_squared_error( y_true = data_test, y_pred = predictions ) print(f"Test error (mse): {error_mse}")

Test error (mse): 0.04487765885818191

Feature importance¶

In [8]:

forecaster.get_feature_importance()

forecaster.get_feature_importance()

Out[8]:

	feature	importance
0	custom_predictor_0	0.539720
1	custom_predictor_1	0.119097
2	custom_predictor_2	0.046404
3	custom_predictor_3	0.024165
4	custom_predictor_4	0.030567
5	custom_predictor_5	0.015139
6	custom_predictor_6	0.042883
7	custom_predictor_7	0.012742
8	custom_predictor_8	0.018938
9	custom_predictor_9	0.108639
10	custom_predictor_10	0.041707

Extract training matrix¶

In [9]:

X, y = forecaster.create_train_X_y(data_train)
X.head()

X, y = forecaster.create_train_X_y(data_train) X.head()

Out[9]:

	custom_predictor_0	custom_predictor_1	custom_predictor_2	custom_predictor_3	custom_predictor_4	custom_predictor_5	custom_predictor_6	custom_predictor_7	custom_predictor_8	custom_predictor_9	custom_predictor_10
datetime
1993-03-01	0.387554	0.751503	0.771258	0.595223	0.568606	0.534761	0.475463	0.483389	0.410534	0.361801	0.496401
1993-04-01	0.427283	0.387554	0.751503	0.771258	0.595223	0.568606	0.534761	0.475463	0.483389	0.410534	0.496275
1993-05-01	0.413890	0.427283	0.387554	0.751503	0.771258	0.595223	0.568606	0.534761	0.475463	0.483389	0.496924
1993-06-01	0.428859	0.413890	0.427283	0.387554	0.751503	0.771258	0.595223	0.568606	0.534761	0.475463	0.496759
1993-07-01	0.470126	0.428859	0.413890	0.427283	0.387554	0.751503	0.771258	0.595223	0.568606	0.534761	0.495638

In [10]:

y.head()

y.head()

Out[10]:

datetime
1993-03-01    0.427283
1993-04-01    0.413890
1993-05-01    0.428859
1993-06-01    0.470126
1993-07-01    0.509210
Freq: MS, Name: y, dtype: float64

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