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Time Series Forecast with DeepAR

In this notebook we will use SageMaker DeepAR to perform time series prediction. The data we will be using is provided by Kaggle; a global household eletric power consumption data set collected over years from 2006 to 2010. A large dataset like this allows us to make time series prediction over long periods of time, like weeks or months.

Data Exploration

Let's get started by exploring the data and see what's contained within the data set.
! wget https://s3.amazonaws.com/video.udacity-data.com/topher/2019/March/5c88a3f1_household-electric-power-consumption/household-electric-power-consumption.zip
--2020-04-22 01:54:44-- https://s3.amazonaws.com/video.udacity-data.com/topher/2019/March/5c88a3f1_household-electric-power-consumption/household-electric-power-consumption.zip
Resolving s3.amazonaws.com (s3.amazonaws.com)... 52.216.142.102
Connecting to s3.amazonaws.com (s3.amazonaws.com)|52.216.142.102|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 20805339 (20M) [application/zip]
Saving to: ‘household-electric-power-consumption.zip’
household-electric- 100%[===================>] 19.84M 8.19MB/s in 2.4s
2020-04-22 01:54:47 (8.19 MB/s) - ‘household-electric-power-consumption.zip’ saved [20805339/20805339]
! unzip household-electric-power-consumption
Archive: household-electric-power-consumption.zip
inflating: household_power_consumption.txt
with open('household_power_consumption.txt') as file:
for line in range(10):
print(next(file))
Date;Time;Global_active_power;Global_reactive_power;Voltage;Global_intensity;Sub_metering_1;Sub_metering_2;Sub_metering_3
16/12/2006;17:24:00;4.216;0.418;234.840;18.400;0.000;1.000;17.000
16/12/2006;17:25:00;5.360;0.436;233.630;23.000;0.000;1.000;16.000
16/12/2006;17:26:00;5.374;0.498;233.290;23.000;0.000;2.000;17.000
16/12/2006;17:27:00;5.388;0.502;233.740;23.000;0.000;1.000;17.000
16/12/2006;17:28:00;3.666;0.528;235.680;15.800;0.000;1.000;17.000
16/12/2006;17:29:00;3.520;0.522;235.020;15.000;0.000;2.000;17.000
16/12/2006;17:30:00;3.702;0.520;235.090;15.800;0.000;1.000;17.000
16/12/2006;17:31:00;3.700;0.520;235.220;15.800;0.000;1.000;17.000
16/12/2006;17:32:00;3.668;0.510;233.990;15.800;0.000;1.000;17.000
We've downloaded a text file which has a similar format to that of CSV except it is separated by ;.

Data Preprocessing

The text file has the following attributes,
  • Each data point has date and time of recording
  • Each feature is separated by ;
  • Some values are either NaN or ?, we'll treat them as NaN in DataFrame
For NaN values, instead of dropping them, we want to fill them with the mean value of that column. This is to ensure our time series is nice and smooth. It's not a terrible assumption to make that if a record is missing, it's likely that the record has a mean value of energy consumption, given that we don't have that many missing values.

Load Text Data into Data Frame

import pandas as pd
df = pd.read_csv('household_power_consumption.txt', sep=';',
parse_dates={'Date-Time': ['Date', 'Time']},
infer_datetime_format=True,
low_memory=False,
na_values=['nan', '?'],
index_col='Date-Time')
print('Number of missing values per column')
df.isnull().sum()
Number of missing values per column
Global_active_power 25979
Global_reactive_power 25979
Voltage 25979
Global_intensity 25979
Sub_metering_1 25979
Sub_metering_2 25979
Sub_metering_3 25979
dtype: int64
print('Numer of values per column')
df.count()
Numer of values per column
Global_active_power 2049280
Global_reactive_power 2049280
Voltage 2049280
Global_intensity 2049280
Sub_metering_1 2049280
Sub_metering_2 2049280
Sub_metering_3 2049280
dtype: int64

Replace NaN with Mean

num_cols = len(list(df.columns.values))
for col in range(num_cols):
df.iloc[:,col]=df.iloc[:,col].fillna(df.iloc[:,col].mean())
print('Number of missing values per column')
df.isnull().sum()
Number of missing values per column
Global_active_power 0
Global_reactive_power 0
Voltage 0
Global_intensity 0
Sub_metering_1 0
Sub_metering_2 0
Sub_metering_3 0
dtype: int64
df.head()
Text
Global_active_power
Global_reactive_power
Voltage
Global_intensity
Sub_metering_1
Sub_metering_2
Sub_metering_3
Date-Time
2006-12-16 17:24:00
4.216
0.418
234.84
18.4
0.0
1.0
17.0
2006-12-16 17:25:00
5.360
0.436
233.63
23.0
0.0
1.0
16.0
2006-12-16 17:26:00
5.374
0.498
233.29
23.0
0.0
2.0
17.0
2006-12-16 17:27:00
5.388
0.502
233.74
23.0
0.0
1.0
17.0
2006-12-16 17:28:00
3.666
0.528
235.68
15.8
0.0
1.0
17.0

Display Global Active Power

For this demonstration, we will predict global active power. We can ignore the other columns.
active_power_df = df['Global_active_power'].copy()
import matplotlib.pyplot as plt
%matplotlib inline
plt.figure(figsize=(12,6))
active_power_df.plot(title='Global Active Power', color='green')
plt.show()
png
The data are recorded each minute, we want to zoom into one day worth of data and see what it looks like.
# There are 1440 minutes in a day
plt.figure(figsize=(12,6))
active_power_df[0:1440].plot(title='Global Active Power Over 1 Day', color='green')
plt.show()
png

Hourly vs Daily

With this amount of data, there are many interesting approaches to this problem.
  1. 1.
    Create many short time series, predict the energy consumption over hours or days.
  2. 2.
    Create fewer but longer time series, predict the energy consumption over seasons.
For the purpose of demonstrating pandas resampling, we will go with the latter. We need to convert the minute data points into hour or day data points. Pandas' time series tools allow us to easily resample time series data by frequency, e.g. hourly H or daily D
# Set frequency to be daily
freq = 'D'
mean_active_power_df = active_power_df.resample(freq).mean()
plt.figure(figsize=(12,6))
mean_active_power_df.plot(title='Global Active Power Mean per Day', color='green')
plt.show()
png

Create Time Series Training Data

The objective is to train a model on a 3 years of data and use the 4th year as the test set to predict what will be the power usage in first few months of 2010. There wil be 3 year-long time series from the years 2007, 2008, and 2009.
def create_time_series_list_by_years(df, years, freq='D', start_idx=0):
"""Creates time series for each supplied year in the years list.
"""
leap = '2008' # We should account for all leap years but for the purpose of this demo, 2008 is enough
time_series_list = []
for i in range(len(years)):
if years[i] == leap:
end_idx = start_idx + 366
else:
end_idx = start_idx + 365
index = pd.date_range(start=years[i] + '-01-01', end=years[i] + '-12-31', freq=freq)
time_series_list.append(pd.Series(data=df[start_idx:end_idx], index=index))
start_idx = end_idx
return time_series_list
Now we can plot the time series and see that there are 3 series, each has length either 365 or 366, depending on whether it is a leap year or not.
time_series_list = create_time_series_list_by_years(mean_active_power_df, ['2007', '2008', '2009'], start_idx=16)
plt.figure(figsize=(12,6))
for ts in time_series_list:
ts.plot()
plt.show()
png

Training Feature/Label Split in Time

This is supervised learning, we need to provide our training set with some labels or targets. One simple way to think about it is to split the year-long time series into two chunks. The first chunk is the training, while the second chunk is the label. We are training a model to accept an input time series and return a prediction time series. Let's call the length of the prediction time series prediction_length.
For example, I have 365 days of data. I want my prediction length to be a month or 30 days. The input time series would have 335 data points while the label or target time series would have 30 data points. This split must occur in time though. We cannot randomly choose 30 days out of 365 days.
prediction_length = 30 # Days
training_list = []
for ts in time_series_list:
training_list.append(ts[:-prediction_length])
for ts in training_list:
print('Training set has shape {} after truncating {} days'.format(ts.shape, prediction_length))
Training set has shape (335,) after truncating 30 days
Training set has shape (336,) after truncating 30 days
Training set has shape (335,) after truncating 30 days
Now let's visualize the split.
plt.figure(figsize=(12,6))
time_series_list[0].plot(label='test', lw=3)
training_list[0].plot(label='train', ls=':', lw=3)
plt.legend()
plt.show()
png

DeepAR

Save as JSON

Before we run DeepAR on SageMaker, we need to do one final data preparation, i.e. converting the data frames into JSON format that is accepted by DeepAR.
DeepAR expects to see input training data in the following JSON fields.
  • start: a string that defines the starting date of the time series YYYY-MM-DD HH:MM:SS
  • target: a list of numerical values that represent the time series
  • cat: optional, a numerical array of categorical features that can be used to encode the groups that the record belongs to. This is useful for finding models per class of item.
For example,
{
"start": "2007-01-01 00:00:00"
"target": [1.1, 2.2, 3.3],
"cat": [1]
}
import json
import os
def write_json_dataset(time_series_list, data_dir, filename):
if not os.path.exists(data_dir):
os.makedirs(data_dir)
with open(os.path.join(data_dir, filename), 'wb') as f:
for ts in time_series_list:
json_line = json.dumps({
'start': str(ts.index[0]),
'target': list(ts)
}) + '\n'
f.write(json_line.encode('utf-8'))
local_data_dir = 'energy_json_data'
write_json_dataset(training_list, local_data_dir, 'train.json')
write_json_dataset(time_series_list, local_data_dir, 'test.json')

Upload to S3

Just as any other built-in models, SageMaker expects the JSON data to be in a S3 bucket during training and inference job.
import boto3
import sagemaker
session = sagemaker.Session(default_bucket='machine-learning-case-studies')
role = sagemaker.get_execution_role()
s3_bucket = session.default_bucket()
s3_prefix = 'deepar-energy-consumption'
print('Instantiated session with default bucket {}'.format(s3_bucket))
train_path = session.upload_data(os.path.join(local_data_dir, 'train.json'), bucket=s3_bucket, key_prefix=s3_prefix)
test_path = session.upload_data(os.path.join(local_data_dir, 'test.json'), bucket=s3_bucket, key_prefix=s3_prefix)
print('Training data are stored in {}'.format(train_path))
print('Test data are stored in {}'.format(test_path))
Instantiated session with default bucket machine-learning-case-studies
Training data are stored in s3://machine-learning-case-studies/deepar-energy-consumption/train.json
Test data are stored in s3://machine-learning-case-studies/deepar-energy-consumption/test.json

DeepAR Estimator

Instantiate an estimator
from sagemaker.amazon.amazon_estimator import get_image_uri
from sagemaker.estimator import Estimator
s3_output_path = 's3://{}/{}'.format(s3_bucket, s3_prefix)
image_name = get_image_uri(boto3.Session().region_name, 'forecasting-deepar')
estimator = Estimator(sagemaker_session=session,
image_name=image_name,
role=role,
train_instance_count=1,
train_instance_type='ml.c4.xlarge',
output_path=s3_output_path)
There are couple hyperparameters we need to set.
  • epochs: The maximum number of times to pass over the data when training.
  • time_freq: The granularity of time series in the dataset, e.g. D for daily.
  • prediction_length: The number of time steps that the model is trained to predict.
  • context_length: The number of data points that the model gets to see before making a prediction.
More information can be found on Deep AR Documentation
hyperparameters = {
'epochs': '50',
'time_freq': 'D', # Day
'prediction_length': '30',
'context_length': '30',
'num_layers': '2',
'num_cells': '50',
'mini_batch_size': '128',
'learning_rate': '0.001',
'early_stopping_patience': '10'
}
estimator.set_hyperparameters(**hyperparameters)
When we provide inputs toe the fit function, if we provide a test dataset, DeepAR will calculate the accuracy metrics for the trained model. This is done by predicting the last prediction_length points of each time series in the test set and comparing it to the actual value of the time series. The computed error metrics will be included as part of the log output.
estimator.fit(inputs={
'train': train_path,
'test': test_path
})
2020-04-22 01:55:18 Starting - Starting the training job...
2020-04-22 01:55:20 Starting - Launching requested ML instances...............
2020-04-22 01:57:54 Starting - Preparing the instances for training......
2020-04-22 01:58:56 Downloading - Downloading input data...
2020-04-22 01:59:35 Training - Training image download completed.
...
Training seconds: 168
Billable seconds: 168
Deploy it and make it ready for inference.
predictor = estimator.deploy(initial_instance_count=1,
instance_type='ml.t2.medium',
content_type='application/json')

Model Evaluation

Generate Predictions

DeepAR predictor expects JSON for inputs, the input should have the following keys.
  • instances: A list of JSON formatted time series
  • configuration optional: A dictionary of configuration information for the response
    • num_samples
    • output_types
    • quantiles
More information on DeepAR Inference Formats
def to_json_predictor_input(ts_list, num_samples=50, quantiles=['0.1', '0.5', '0.9']):
instances = []
for i in range(len(ts_list)):
instances.append({
'start': str(ts_list[i].index[0]),
'target': list(ts_list[i])
})
configuration = {
'output_types': ['quantiles'],
'num_samples': num_samples,
'quantiles': quantiles
}
json_request = json.dumps({
'instances': instances,
'configuration': configuration
}).encode('utf-8')
return json_request
prediction = predictor.predict(to_json_predictor_input(training_list))
The prediction JSON would look something like the following. We need to decode string into JSON object and then load the data into a DataFrame.
{
"predictions":[
{
"quantiles":{
"0.1":[numbers...],
"0.9":[numbers...],
"0.5":[numbers...]
}
},
{
"quantiles":{
"0.1":[numbers...],
"0.9":[numbers...],
"0.5":[numbers...]
}
},
{
"quantiles":{
"0.1":[numbers...],
"0.9":[numbers...],
"0.5":[numbers...]
}
}
]
}
def decode_prediction(prediction_json, encoding='utf-8'):
json_data = json.loads(prediction_json.decode(encoding))
pd_list = []
for i in range(len(json_data['predictions'])):
pd_list.append(pd.DataFrame(data=json_data['predictions'][i]['quantiles']))
return pd_list
results = decode_prediction(prediction)
print(results[0])
0.1 0.9 0.5
0 1.088849 1.828646 1.502092
1 1.022624 1.551457 1.282422
2 1.305507 1.900849 1.538800
3 1.033439 1.458136 1.265341
4 0.873974 1.416162 1.145868
5 1.183307 1.786330 1.489765
6 1.358921 1.893588 1.658125
7 1.023880 1.776468 1.353857
8 0.816724 1.451042 1.169706
9 0.989871 1.643647 1.341426
10 0.954561 1.471301 1.141534
11 0.882861 1.549521 1.137189
12 1.066164 1.888121 1.350396
13 1.008170 1.875113 1.396235
14 1.088461 2.301020 1.586839
15 1.058260 1.541146 1.302700
16 1.191627 1.804835 1.495359
17 1.081382 1.848557 1.330365
18 0.916280 1.782442 1.443959
19 1.096332 1.851947 1.503171
20 1.189601 2.429537 1.660283
21 0.864012 2.223838 1.407799
22 0.906846 1.819857 1.251403
23 1.061913 1.900574 1.439155
24 0.908473 1.759717 1.377292
25 0.656686 1.697152 1.085249
26 0.702556 2.054829 1.307726
27 0.484136 1.871403 1.430208
28 0.824297 1.891673 1.406972
29 0.671941 1.601008 1.103163

Visualize the Results

  • Quantiles 0.1 and 0.9 represent higher and lower bounds for the predicted values.
  • Quantile 0.5 represents the median of all sample predictions.
def display_quantiles(prediction_list, target=None, prediction_length=30):
for i in range(len(prediction_list)):
plt.figure(figsize=(12, 6))
if target is not None:
target_ts = target[i][-prediction_length:]
plt.plot(range(len(target_ts)), target_ts, label='target')
p10 = prediction_list[i]['0.1']
p90 = prediction_list[i]['0.9']
plt.fill_between(p10.index, p10, p90, color='y', alpha=0.5, label='80% confidence interval')
prediction_list[i]['0.5'].plot(label='prediction median')
plt.legend()
plt.show()
display_quantiles(results, target=time_series_list)
png
png
png

Predicting the Future

Now we have verified that the predictor works and it can capture patterns fairly well, we can use it to predict the future, i.e. the months in 2010. We will leave target empty and reserve the 2010 data for testing. In fact we could provide the historical data as target and let the model to predict the future.
start_date = '2010-01-01' # We want to predict first 30 days in 2010
timestamp = '00:00:00'
request_data = {
'instances': [
{ 'start': '{} {}'.format(start_date, timestamp), 'target': [] }
],
'configuration': {
'num_samples': 50,
'output_types': ['quantiles'],
'quantiles': ['0.1', '0.5', '0.9']
}
}
predictions_2010 = decode_prediction(predictor.predict(json.dumps(request_data).encode('utf-8')))
pd_series_2010 = pd.Series(data=mean_active_power_df.values[1112:],
index=pd.date_range(start=start_date,
periods=len(mean_active_power_df.values[1112:]),
freq=freq)
)
display_quantiles(predictions_2010, [pd_series_2010[0:prediction_length]])
png
The result came out to be not too bad! Now it's time to clean up.
predictor.delete_endpoint()