How to forecast unknown future target values with gluonts DeepAR? I have a time series from 1995-01-01 to 2021-10-01. Monthly frequency data. How to forecast values for the future (next 3 months): 2021-11-01 to 2022-01-01? Note that I don't have the target values for 2021-11-01, 2021-12-01 and 2022-01-01. Many thanks!
from gluonts.model.deepar import DeepAREstimator
from gluonts.mx import Trainer
import numpy as np
import mxnet as mx
np.random.seed(7)
mx.random.seed(7)
estimator = DeepAREstimator(
prediction_length=12
, context_length=120
, freq='M'
, trainer=Trainer(
epochs=5
, learning_rate=1e-03
, num_batches_per_epoch=50))
predictor = estimator.train(training_data=df_train)
# Forecasting
predictions = predictor.predict(df_test)
predictions = list(predictions)[0]
predictions = predictions.quantile(0.5)
print(predictions)
[163842.34 152805.08 161326.3 176823.97 127003.79 126937.78
139575.2 117121.67 115754.67 139211.28 122623.586 120102.65 ]
As I understood, the predictions values are not for "2021-11-01", "2021-12-01" and "2022-01-01". How do I know to which months this values refer to? How to forecast values for the next 3 months: "2021-11-01", "2021-12-01" and "2022-01-01"?
Take a look at this code. It comes from "Advanced Forecasting with Python". https://github.com/Apress/advanced-forecasting-python/blob/main/Chapter%2020%20-%20Amazon's%20DeepAR.ipynb
It does not seem to forecast unknown future values, once it compares the last 28 values of test_ds (Listing 20-5. R2 score and prediction graph) with the predictions made over this same dataset test_ds (Listing 20-4. Prediction)
How do I forecast unknown future values?
Many thanks!
Data source
https://www.kaggle.com/c/recruit-restaurant-visitor-forecasting
# Listing 20-1. Importing the data
import pandas as pd
y = pd.read_csv('air_visit_data.csv.zip')
y = y.pivot(index='visit_date', columns='air_store_id')['visitors']
y = y.fillna(0)
y = pd.DataFrame(y.sum(axis=1))
y = y.reset_index(drop=False)
y.columns = ['date', 'y']
# Listing 20-2. Preparing the data format requered by the gluonts library
from gluonts.dataset.common import ListDataset
start = pd.Timestamp("01-01-2016", freq="H")
# train dataset: cut the last window of length "prediction_length", add "target" and "start" fields
train_ds = ListDataset([{'target': y.loc[:450,'y'], 'start': start}], freq='H')
# test dataset: use the whole dataset, add "target" and "start" fields
test_ds = ListDataset([{'target': y['y'], 'start': start}],freq='H')
# Listing 20-3. Fitting the default DeepAR model
from gluonts.model.deepar import DeepAREstimator
from gluonts.trainer import Trainer
import mxnet as mx
import numpy as np
np.random.seed(7)
mx.random.seed(7)
estimator = DeepAREstimator(
prediction_length=28,
context_length=100,
freq=’H’,
trainer=Trainer(ctx="gpu", # remove if running on windows
epochs=5,
learning_rate=1e-3,
num_batches_per_epoch=100
)
)
predictor = estimator.train(train_ds)
# Listing 20-4. Prediction
predictions = predictor.predict(test_ds)
predictions = list(predictions)[0]
predictions = predictions.quantile(0.5)
# Listing 20-5. R2 score and prediction graph
from sklearn.metrics import r2_score
print(r2_score( list(test_ds)[0]['target'][-28:], predictions))
import matplotlib.pyplot as plt
plt.plot(predictions)
plt.plot(list(test_ds)[0]['target'][-28:])
plt.legend(['predictions', 'actuals'])
plt.show()
