CODEWITHSUNDEEP
amazon-web-servicesamazon-sagemaker
Hang Nguyen
Hang Nguyen

Reputation: 57

Batch Transform Error for deepAR algorithm

Describe the bug Hi, I'm running the batch transforms from this Amazon SageMaker Batch Transform for the deepAR model implementation in the tutorial Stock Price Prediction, using SageMaker DeepAR. Batch transform code

from sagemaker.transformer import Transformer
model = session.create_model_from_job(estimator._current_job_name, name='{}-test'.format(estimator._current_job_name))

test_transformer = Transformer(model,
                                   1,
                                   'ml.m4.xlarge',
                                   output_path='s3://sagemaker-eu-west-1-xxxxxxxxxxxx/sagemaker/stock-prediction/output',
                                   sagemaker_session=session,
                                   strategy='BatchStrategy',
                                   assemble_with='Line')
test_transformer.transform('s3://sagemaker-eu-west-1-xxxxxxxxxxxx/sagemaker/stock-prediction/source/D/test/test.json', split_type='Line')
test_transformer.wait()

JSON input

{"start": "2018-07-24 00:00:00", "target": [81.73, 79.86, 83.39, 82.91, 82.91, 82.97, 82.97, 82.69, 81.5, 81.18, 82.34, 82.34, 83.51, 83.51, 84.05, 84.48, 84.81, 83.58, 83.58, 83.29, 83.29, 82.45, 81.31, 81.8, 81.41, 81.41, 81.75, 81.75, 82.93, 82.29, 81.08, 81.29, 81.29, 83.19, 83.19, 84.39, 84.23, 84.6, 83.41, 83.41, 82.79, 82.79, 81.88, 81.25, 80.73, 81.07, 81.07, 81.1, 81.1, 80.76, 81.32, 82.47, 82.93, 82.93, 82.54, 82.54, 82.67, 83.54, 85.32, 85.77, 85.77, 83.5, 83.5, 79.0, 79.03, 79.0, 77.71, 77.71, 78.14, 78.14, 78.7, 78.26, 78.26, 77.68, 77.68, 76.87, 76.87, 76.39, 75.35, 74.3, 74.49, 74.49, 75.21, 75.21, 75.75, 75.24, 75.13, 74.64, 74.64, 74.44, 74.44, 73.5, 72.69, 74.5, 75.02, 75.02, 76.4, 76.4], "dynamic_feat": [[82.2, 81.31, 83.39, 84.23, 84.23, 83.12, 83.12, 83.27, 82.28, 81.66, 83.02, 83.02, 84.38, 84.38, 84.79, 84.68, 85.42, 84.19, 84.19, 84.02, 84.02, 83.51, 82.93, 82.15, 81.78, 81.78, 82.45, 82.45, 83.15, 83.66, 81.76, 81.46, 81.46, 83.33, 83.33, 85.22, 84.68, 85.75, 83.98, 83.98, 82.89, 82.89, 82.95, 82.11, 81.84, 81.19, 81.19, 81.5, 81.5, 81.05, 81.45, 83.35, 83.25, 83.25, 83.05, 83.05, 83.81, 83.82, 85.48, 86.74, 86.74, 85.26, 85.26, 83.2, 79.03, 79.16, 78.44, 78.44, 78.7, 78.7, 79.44, 78.99, 78.99, 77.94, 77.94, 77.39, 77.39, 76.78, 76.0, 75.21, 75.52, 75.52, 75.86, 75.86, 75.98, 75.47, 76.4, 75.2, 75.2, 75.94, 75.94, 74.53, 74.09, 74.97, 75.02, 75.02, 79.38, 79.38], [81.06, 79.38, 81.22, 82.45, 82.45, 82.51, 82.51, 81.97, 80.88, 79.22, 82.11, 82.11, 82.86, 82.86, 84.05, 83.53, 84.57, 82.9, 82.9, 83.28, 83.28, 81.92, 80.79, 81.3, 80.46, 80.46, 81.73, 81.73, 81.95, 81.18, 80.92, 80.76, 80.76, 82.43, 82.43, 84.26, 83.52, 83.17, 83.21, 83.21, 82.15, 82.15, 81.44, 81.03, 80.43, 80.46, 80.46, 80.65, 80.65, 80.18, 80.37, 82.28, 82.42, 82.42, 82.04, 82.04, 82.26, 82.56, 83.85, 85.26, 85.26, 83.4, 83.4, 78.51, 77.35, 78.18, 77.32, 77.32, 78.09, 78.09, 78.0, 78.05, 78.05, 77.24, 77.24, 76.72, 76.72, 75.66, 75.12, 73.74, 74.3, 74.3, 73.8, 73.8, 75.25, 74.65, 75.06, 73.08, 73.08, 74.07, 74.07, 72.85, 72.44, 73.48, 73.62, 73.62, 75.33, 75.33], [81.13, 81.1, 81.59, 84.18, 84.18, 82.55, 82.55, 82.6, 81.88, 79.95, 82.32, 82.32, 82.96, 82.96, 84.56, 84.09, 84.77, 83.83, 83.83, 83.48, 83.48, 83.46, 82.86, 81.86, 81.46, 81.46, 82.1, 82.1, 82.06, 83.43, 81.76, 81.26, 81.26, 82.44, 82.44, 84.85, 84.57, 83.59, 83.63, 83.63, 82.46, 82.46, 82.68, 81.45, 81.48, 80.85, 80.85, 80.67, 80.67, 81.02, 81.25, 82.42, 82.58, 82.58, 82.14, 82.14, 83.5, 82.72, 83.93, 86.52, 86.52, 85.21, 85.21, 82.98, 77.98, 78.19, 77.59, 77.59, 78.31, 78.31, 78.85, 78.55, 78.55, 77.64, 77.64, 77.16, 77.16, 76.65, 75.89, 73.86, 74.63, 74.63, 74.49, 74.49, 75.42, 75.27, 76.19, 74.16, 74.16, 75.89, 75.89, 73.16, 73.42, 73.59, 73.86, 73.86, 75.48, 75.48]]}
{"start": "2018-07-24 00:00:00", "target": [196.9, 193.9, 197.8, 197.3, 197.3, 197.45, 197.45, 197.0, 189.85, 186.45, 184.45, 184.45, 186.65, 186.65, 188.15, 188.05, 189.0, 186.5, 186.5, 187.45, 187.45, 185.9, 182.9, 184.05, 182.65, 182.65, 184.9, 184.9, 185.3, 160.85, 153.95, 155.45, 155.45, 160.1, 160.1, 159.9, 160.35, 161.1, 158.05, 158.05, 156.6, 156.6, 155.15, 152.7, 151.4, 150.15, 150.15, 150.35, 150.35, 147.0, 149.6, 149.35, 150.7, 150.7, 151.1, 151.1, 150.0, 151.9, 158.0, 159.1, 159.1, 156.8, 156.8, 152.9, 151.7, 151.5, 149.95, 149.95, 151.5, 151.5, 152.75, 146.95, 146.95, 144.3, 144.3, 143.85, 143.85, 143.1, 139.2, 137.0, 140.15, 140.15, 138.4, 138.4, 140.6, 138.95, 137.75, 131.5, 131.5, 133.3, 133.3, 133.3, 129.45, 138.5, 136.1, 136.1, 143.55, 143.55], "dynamic_feat": [[197.25, 196.25, 198.1, 198.35, 198.35, 198.3, 198.3, 197.7, 196.45, 186.8, 187.75, 187.75, 188.65, 188.65, 189.35, 188.4, 190.05, 187.1, 187.1, 188.45, 188.45, 187.25, 186.35, 185.0, 183.9, 183.9, 186.05, 186.05, 185.65, 186.1, 158.95, 155.95, 155.95, 161.4, 161.4, 160.15, 160.7, 162.45, 159.55, 159.55, 157.6, 157.6, 157.55, 155.2, 153.55, 152.15, 152.15, 151.9, 151.9, 150.55, 149.85, 151.5, 151.35, 151.35, 151.8, 151.8, 151.7, 152.45, 158.4, 160.35, 160.35, 157.95, 157.95, 156.85, 151.9, 151.7, 151.1, 151.1, 152.75, 152.75, 154.25, 148.25, 148.25, 144.45, 144.45, 144.0, 144.0, 145.1, 142.0, 140.1, 141.3, 141.3, 140.55, 140.55, 142.3, 139.45, 140.75, 133.0, 133.0, 134.6, 134.6, 134.25, 133.85, 140.95, 136.2, 136.2, 146.65, 146.65], [194.0, 192.5, 195.7, 196.15, 196.15, 196.75, 196.75, 195.25, 189.3, 183.0, 184.05, 184.05, 185.25, 185.25, 187.7, 186.55, 187.85, 185.35, 185.35, 186.05, 186.05, 184.55, 182.05, 183.15, 181.25, 181.25, 184.6, 184.6, 184.0, 157.9, 153.35, 154.1, 154.1, 157.05, 157.05, 157.75, 159.3, 159.1, 157.95, 157.95, 155.95, 155.95, 154.45, 149.75, 151.35, 148.85, 148.85, 150.25, 150.25, 146.65, 146.75, 149.25, 149.25, 149.25, 150.25, 150.25, 148.8, 149.65, 154.0, 158.3, 158.3, 156.35, 156.35, 151.6, 148.5, 149.65, 148.95, 148.95, 150.4, 150.4, 151.15, 145.05, 145.05, 141.4, 141.4, 142.5, 142.5, 142.35, 138.8, 136.1, 137.6, 137.6, 137.5, 137.5, 139.35, 137.75, 137.7, 127.9, 127.9, 132.25, 132.25, 130.6, 129.15, 130.9, 133.1, 133.1, 135.9, 135.9], [194.15, 195.75, 196.05, 198.2, 198.2, 196.85, 196.85, 196.7, 195.65, 183.6, 186.9, 186.9, 185.8, 185.8, 188.5, 187.5, 188.3, 186.5, 186.5, 186.35, 186.35, 186.95, 186.15, 184.25, 182.6, 182.6, 184.9, 184.9, 184.0, 185.65, 158.5, 155.25, 155.25, 157.8, 157.8, 159.8, 159.9, 159.95, 158.9, 158.9, 156.9, 156.9, 157.25, 154.3, 151.85, 151.85, 151.85, 150.55, 150.55, 150.2, 148.6, 149.9, 151.15, 151.15, 150.5, 150.5, 151.55, 150.05, 154.1, 159.65, 159.65, 157.6, 157.6, 156.25, 150.75, 150.3, 150.15, 150.15, 151.1, 151.1, 152.2, 148.25, 148.25, 143.6, 143.6, 143.4, 143.4, 144.75, 141.7, 136.1, 139.05, 139.05, 140.15, 140.15, 139.4, 139.4, 140.55, 132.7, 132.7, 134.55, 134.55, 131.6, 133.1, 131.4, 135.35, 135.35, 136.8, 136.8]]}
{"start": "2018-07-24 00:00:00", "target": [59.14, 57.88, 59.51, 59.29, 59.29, 59.35, 59.35, 59.15, 58.24, 57.41, 58.16, 58.16, 58.25, 58.25, 58.89, 59.06, 59.04, 57.2, 57.2, 57.23, 57.23, 56.45, 55.05, 55.29, 54.92, 54.92, 55.29, 55.29, 55.87, 55.3, 54.41, 54.75, 54.75, 56.11, 56.11, 56.3, 56.67, 56.65, 55.7, 55.7, 55.0, 55.0, 54.2, 54.28, 54.32, 54.48, 54.48, 54.5, 54.5, 54.05, 54.53, 55.09, 55.54, 55.54, 55.41, 55.41, 55.77, 56.32, 57.21, 57.61, 57.61, 56.13, 56.13, 54.74, 54.83, 55.59, 54.35, 54.35, 54.88, 54.88, 56.0, 56.44, 56.44, 55.61, 55.61, 54.6, 54.6, 54.42, 53.6, 52.7, 52.89, 52.89, 52.92, 52.92, 53.23, 52.76, 52.41, 51.39, 51.39, 50.81, 50.81, 50.48, 50.0, 51.35, 51.37, 51.37, 52.43, 52.43], "dynamic_feat": [[59.72, 58.79, 59.75, 59.6, 59.6, 59.62, 59.62, 59.59, 58.85, 57.71, 58.53, 58.53, 58.99, 58.99, 59.4, 59.27, 59.38, 58.11, 58.11, 57.86, 57.86, 57.35, 56.71, 55.72, 55.29, 55.29, 55.59, 55.59, 55.99, 56.37, 54.97, 55.0, 55.0, 56.21, 56.21, 56.98, 56.73, 57.33, 56.27, 56.27, 55.44, 55.44, 54.98, 54.66, 55.09, 54.5, 54.5, 54.68, 54.68, 54.45, 54.54, 55.61, 56.14, 56.14, 55.85, 55.85, 56.47, 56.47, 57.56, 58.81, 58.81, 57.08, 57.08, 56.16, 54.88, 55.61, 55.36, 55.36, 55.1, 55.1, 56.43, 57.09, 57.09, 55.94, 55.94, 55.26, 55.26, 54.6, 54.3, 53.39, 53.84, 53.84, 53.21, 53.21, 53.54, 52.88, 53.52, 52.25, 52.25, 52.22, 52.22, 50.86, 51.35, 51.77, 51.37, 51.37, 54.53, 54.53], [58.42, 57.31, 57.82, 58.89, 58.89, 59.17, 59.17, 58.82, 58.06, 56.76, 57.69, 57.69, 58.09, 58.09, 58.85, 58.59, 58.84, 56.97, 56.97, 57.23, 57.23, 56.06, 54.75, 55.05, 54.22, 54.22, 55.16, 55.16, 54.84, 54.36, 54.35, 54.35, 54.35, 55.17, 55.17, 56.3, 56.01, 55.69, 55.63, 55.63, 54.75, 54.75, 53.86, 53.87, 54.25, 54.1, 54.1, 54.0, 54.0, 53.75, 53.78, 55.02, 55.16, 55.16, 55.13, 55.13, 55.32, 55.84, 56.51, 57.36, 57.36, 56.05, 56.05, 54.22, 53.73, 54.47, 54.12, 54.12, 54.33, 54.33, 55.26, 56.27, 56.27, 55.37, 55.37, 54.54, 54.54, 53.63, 53.52, 52.13, 52.75, 52.75, 52.03, 52.03, 53.0, 52.28, 52.37, 48.77, 48.77, 50.81, 50.81, 49.78, 49.84, 50.57, 50.34, 50.34, 51.37, 51.37], [58.43, 58.59, 57.86, 59.54, 59.54, 59.21, 59.21, 59.46, 58.54, 57.16, 57.78, 57.78, 58.31, 58.31, 59.25, 58.88, 59.0, 57.81, 57.81, 57.52, 57.52, 57.26, 56.69, 55.48, 55.1, 55.1, 55.51, 55.51, 54.92, 56.14, 54.88, 54.84, 54.84, 55.18, 55.18, 56.62, 56.35, 55.83, 55.94, 55.94, 55.21, 55.21, 54.85, 53.91, 54.5, 54.37, 54.37, 54.09, 54.09, 54.44, 54.11, 55.35, 55.44, 55.44, 55.26, 55.26, 56.37, 56.09, 56.73, 58.56, 58.56, 57.03, 57.03, 55.97, 53.86, 54.47, 54.94, 54.94, 54.35, 54.35, 56.06, 56.7, 56.7, 55.89, 55.89, 55.11, 55.11, 54.52, 54.13, 52.15, 53.22, 53.22, 52.89, 52.89, 53.14, 52.77, 53.31, 51.83, 51.83, 52.07, 52.07, 50.09, 50.83, 50.69, 50.74, 50.74, 51.4, 51.4]]}
{"start": "2018-07-24 00:00:00", "target": [57.1, 55.92, 58.18, 58.38, 58.38, 58.32, 58.32, 57.9, 56.2, 55.02, 56.02, 56.02, 56.0, 56.0, 55.86, 57.14, 56.88, 55.42, 55.42, 55.06, 55.06, 54.8, 53.74, 53.72, 53.4, 53.4, 53.76, 53.76, 54.42, 53.92, 53.24, 53.44, 53.44, 55.02, 55.02, 55.76, 55.44, 55.28, 54.52, 54.52, 53.6, 53.6, 52.9, 52.7, 52.22, 52.06, 52.06, 52.0, 52.0, 51.6, 51.7, 52.84, 54.72, 54.72, 55.56, 55.56, 57.14, 58.36, 59.08, 59.12, 59.12, 58.34, 58.34, 57.54, 57.58, 58.72, 58.0, 58.0, 58.4, 58.4, 58.3, 57.9, 57.9, 57.1, 57.1, 56.04, 56.04, 56.06, 54.52, 53.42, 53.4, 53.4, 54.84, 54.84, 57.0, 56.46, 55.9, 55.0, 55.0, 54.62, 54.62, 53.88, 50.78, 51.76, 51.56, 51.56, 53.52, 53.52], "dynamic_feat": [[57.42, 56.92, 58.36, 58.72, 58.72, 58.46, 58.46, 58.44, 57.58, 55.4, 56.44, 56.44, 56.64, 56.64, 57.22, 57.24, 57.36, 56.32, 56.32, 55.46, 55.46, 55.52, 54.98, 54.38, 53.78, 53.78, 54.34, 54.34, 54.62, 55.2, 53.8, 53.46, 53.46, 55.36, 55.36, 56.08, 55.54, 56.32, 54.94, 54.94, 54.52, 54.52, 53.74, 53.16, 53.08, 52.06, 52.06, 52.22, 52.22, 52.0, 51.82, 53.28, 55.46, 55.46, 55.8, 55.8, 57.56, 58.44, 59.44, 60.46, 60.46, 58.84, 58.84, 59.02, 57.62, 59.24, 58.2, 58.2, 58.94, 58.94, 58.8, 58.62, 58.62, 57.52, 57.52, 56.48, 56.48, 56.6, 55.76, 53.86, 54.38, 54.38, 55.64, 55.64, 57.66, 56.94, 57.16, 55.32, 55.32, 56.0, 56.0, 54.0, 53.52, 52.08, 51.56, 51.56, 54.72, 54.72], [55.94, 55.88, 57.54, 57.98, 57.98, 58.1, 58.1, 57.7, 55.78, 54.38, 55.5, 55.5, 55.8, 55.8, 55.86, 55.86, 56.74, 54.44, 54.44, 54.68, 54.68, 54.34, 53.3, 53.32, 52.36, 52.36, 53.62, 53.62, 53.7, 53.34, 53.12, 52.88, 52.88, 53.88, 53.88, 55.64, 54.82, 54.42, 54.32, 54.32, 52.98, 52.98, 52.38, 52.48, 52.14, 51.22, 51.22, 51.66, 51.66, 51.32, 51.44, 51.82, 53.44, 53.44, 54.94, 54.94, 56.62, 57.4, 58.34, 58.66, 58.66, 58.08, 58.08, 56.9, 56.66, 57.36, 57.2, 57.2, 58.1, 58.1, 57.68, 57.74, 57.74, 56.96, 56.96, 56.02, 56.02, 55.42, 54.32, 52.86, 53.26, 53.26, 53.2, 53.2, 53.86, 55.74, 55.84, 53.48, 53.48, 54.26, 54.26, 53.08, 50.78, 51.24, 50.42, 50.42, 51.56, 51.56], [56.0, 56.48, 58.08, 58.18, 58.18, 58.22, 58.22, 58.08, 57.14, 54.9, 55.56, 55.56, 56.44, 56.44, 57.0, 56.04, 57.28, 55.86, 55.86, 54.74, 54.74, 55.5, 54.96, 54.1, 53.34, 53.34, 53.92, 53.92, 53.76, 54.86, 53.8, 53.24, 53.24, 53.9, 53.9, 55.88, 55.54, 54.7, 54.42, 54.42, 54.52, 54.52, 53.46, 52.76, 52.7, 52.06, 52.06, 51.68, 51.68, 52.0, 51.7, 51.9, 53.56, 53.56, 55.1, 55.1, 56.96, 57.54, 58.46, 60.26, 60.26, 58.44, 58.44, 58.74, 56.82, 57.36, 57.64, 57.64, 58.28, 58.28, 58.62, 58.34, 58.34, 57.44, 57.44, 56.32, 56.32, 56.44, 55.6, 53.06, 54.2, 54.2, 53.4, 53.4, 55.76, 56.92, 56.96, 55.2, 55.2, 55.92, 55.92, 53.42, 53.16, 51.6, 51.16, 51.16, 51.56, 51.56]]}
{"start": "2018-07-24 00:00:00", "target": [150.38, 145.92, 151.7, 151.12, 151.12, 151.46, 151.46, 152.22, 146.6, 143.4, 145.34, 145.34, 145.48, 145.48, 146.88, 147.82, 147.84, 144.38, 144.38, 143.98, 143.98, 142.9, 138.44, 139.44, 138.74, 138.74, 139.0, 139.0, 140.72, 138.82, 137.94, 138.0, 138.0, 141.46, 141.46, 143.38, 143.16, 142.86, 140.84, 140.84, 137.94, 137.94, 136.2, 136.14, 136.24, 136.08, 136.08, 137.5, 137.5, 137.38, 138.4, 140.78, 144.0, 144.0, 144.48, 144.48, 148.24, 151.1, 152.5, 154.38, 154.38, 152.94, 152.94, 150.48, 151.3, 153.86, 151.6, 151.6, 152.5, 152.5, 152.94, 152.08, 152.08, 149.3, 149.3, 147.0, 147.0, 146.54, 143.54, 140.48, 140.16, 140.16, 144.42, 144.42, 147.42, 147.1, 145.58, 143.98, 143.98, 142.34, 142.34, 139.7, 133.7, 137.0, 136.88, 136.88, 142.42, 142.42], "dynamic_feat": [[151.6, 148.98, 152.34, 153.42, 153.42, 152.2, 152.2, 153.36, 151.6, 143.9, 145.62, 145.62, 147.08, 147.08, 149.1, 148.72, 148.8, 146.78, 146.78, 144.92, 144.92, 144.68, 143.36, 140.74, 139.32, 139.32, 140.62, 140.62, 141.16, 142.3, 138.52, 138.4, 138.4, 142.14, 142.14, 144.92, 143.52, 145.36, 141.4, 141.4, 139.18, 139.18, 138.3, 137.2, 138.04, 136.32, 136.32, 138.94, 138.94, 137.76, 138.56, 141.88, 144.7, 144.7, 145.26, 145.26, 150.24, 151.96, 154.1, 157.2, 157.2, 153.62, 153.62, 155.0, 151.54, 155.68, 152.52, 152.52, 154.46, 154.46, 154.46, 154.26, 154.26, 150.68, 150.68, 147.76, 147.76, 148.06, 146.1, 142.8, 142.62, 142.62, 146.72, 146.72, 149.32, 147.4, 149.3, 144.84, 144.84, 146.78, 146.78, 140.74, 139.54, 137.88, 136.88, 136.88, 146.34, 146.34], [148.84, 145.26, 149.66, 150.52, 150.52, 150.92, 150.92, 150.7, 145.36, 140.38, 143.34, 143.34, 144.92, 144.92, 146.8, 146.04, 146.64, 143.24, 143.24, 143.68, 143.68, 142.34, 137.46, 138.9, 136.54, 136.54, 139.0, 139.0, 138.02, 136.66, 137.52, 136.86, 136.86, 139.3, 139.3, 143.38, 141.64, 140.66, 140.12, 140.12, 136.42, 136.42, 134.9, 135.42, 135.54, 134.2, 134.2, 135.6, 135.6, 136.04, 136.56, 140.68, 141.14, 141.14, 143.2, 143.2, 146.5, 149.3, 151.9, 152.8, 152.8, 151.2, 151.2, 149.06, 147.94, 150.44, 149.86, 149.86, 152.08, 152.08, 151.68, 151.86, 151.86, 149.02, 149.02, 146.56, 146.56, 144.54, 142.88, 138.1, 140.12, 140.12, 139.66, 139.66, 145.0, 145.52, 145.4, 139.16, 139.16, 141.68, 141.68, 138.54, 133.0, 135.0, 134.02, 134.02, 137.92, 137.92], [149.02, 148.64, 150.44, 153.32, 153.32, 151.82, 151.82, 151.32, 150.12, 142.12, 143.76, 143.76, 145.3, 145.3, 147.72, 146.94, 147.5, 145.56, 145.56, 144.06, 144.06, 144.54, 143.34, 140.06, 138.06, 138.06, 140.3, 140.3, 138.3, 141.78, 138.16, 138.08, 138.08, 139.34, 139.34, 144.72, 143.38, 141.74, 140.48, 140.48, 138.26, 138.26, 138.0, 136.24, 136.1, 135.94, 135.94, 135.6, 135.6, 137.46, 137.92, 140.98, 141.38, 141.38, 143.64, 143.64, 149.14, 149.66, 152.2, 156.9, 156.9, 151.72, 151.72, 153.92, 148.38, 150.46, 151.34, 151.34, 152.58, 152.58, 154.06, 153.48, 153.48, 150.46, 150.46, 147.6, 147.6, 147.8, 145.62, 138.32, 141.8, 141.8, 140.16, 140.16, 146.96, 147.4, 148.96, 143.52, 143.52, 146.56, 146.56, 139.58, 138.58, 136.08, 135.3, 135.3, 137.98, 137.98]]}

JSON.out And when I open the output file JSON.out, it has 5 lines like the one below. (each line for each stock unique instance I guess). I also got this error when run batch transforms on Console.

{"error":"The field dynamic_feat needs to be provided in the full prediction range but request has dynamic_feat only for 0 time units in the prediction range when trying to predict for 91 time units."}

To reproduce Replace the Forecasting and Plotting code in the deepAR notebook with Batch Transform.

Expected behavior A clear and concise description of what you expected to happen. JSON output has values of 3 months for targeted stock.

System information

Upvotes: 1

Views: 659

Answers (2)

João Silva
João Silva

Reputation: 41

Precisely!

If you train with dynamic features, you're basically giving the model an auxiliary regression. That is: For the model to predict x periods in the future, it will need the values of the auxiliary regression on those x periods.

So, if you trained with dynamic features, imagine you are doing inference with a time series of 10 time steps and predicting the next 3.

Your input of the time series will be (length 10):

[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]

But your input dynamic feature series must be (length 13):

[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, x1, x2. x3]

Upvotes: 2

Natrave Drova
Natrave Drova

Reputation: 117

I think you had a description of what is expected by the batch transform job.

Batch transform predicts future values. Since you have prediction horizon of 91 time units (mentioned in the error message), it is expected that you provide dynamic features for all that time units.

As stated in the documentation:

If the model was trained with the dynamic_feat field, you must provide this field for inference. In addition, each of the features has to have the length of the provided target plus the prediction_length. In other words, you must provide the feature value in the future.

Now your target and each dynamic feature have 97 values. It is expected that you provide additional 91 values for each dynamic feature. Taking into account these additional 91 values, predictions of the target will be made.

Upvotes: 2

Related Questions