View source for TimeSeries: Using Tensorflow for time series modelling and forecasting



This is a quick example on using TensorFlow to learn about a time series and perform forecasting. This can be used as an entry point for detecting anomalies.

Module import

 import numpy as np
 import tensorflow as tf
 from tensorflow.contrib.timeseries.python.timeseries import NumpyReader
 tf.logging.set_verbosity(tf.logging.WARN)
 tf.__version__

1.4.0

Plots customisation

 %matplotlib inline
 import matplotlib
 import matplotlib.pyplot as plt
 import seaborn as sns
 sns.set()
 sns.set_context("notebook", font_scale=1.2, rc={"lines.linewidth": 1.5})

Create time series

In this section we create the time series that we are going to use for training, fitting and prediction.

 steps = 200
 x = np.array(range(steps))
 noise = np.random.uniform(-0.25, 0.25, steps)
 y = np.sin(np.pi * x / 10) + np.cos(np.pi * x / 10) + x / 200. + noise
 plt.figure(figsize=(12,6))
 plt.xlabel('time step')
 plt.ylabel('f')
 _ = plt.plot(x, y)

Example image
Train network

 data = {
     tf.contrib.timeseries.TrainEvalFeatures.TIMES: x,
     tf.contrib.timeseries.TrainEvalFeatures.VALUES: y
 }
 
 reader = NumpyReader(data) 
 
 train_input_fn = tf.contrib.timeseries.RandomWindowInputFn(
         reader, batch_size=20, window_size=30)
 
 ar = tf.contrib.timeseries.ARRegressor(
         periodicities=20, input_window_size=20, output_window_size=10,
         num_features=1,
         loss=tf.contrib.timeseries.ARModel.NORMAL_LIKELIHOOD_LOSS) 

 _ = ar.train(input_fn=train_input_fn, steps=600)

Evaluate network

 evaluation_input_fn = tf.contrib.timeseries.WholeDatasetInputFn(reader)
 evaluation = ar.evaluate(input_fn=evaluation_input_fn, steps=10)

Predictions

 (predictions,) = tuple(ar.predict(
         input_fn=tf.contrib.timeseries.predict_continuation_input_fn(
 evaluation, steps=40)))

Plotting

 plt.figure(figsize=(12,6))
 x = data['times'].reshape(-1)
 y = data['values'].reshape(-1)
 plt.plot(x, data['values'].reshape(-1), ':', label='origin')
 
 x = evaluation['times'].reshape(-1)
 y = evaluation['mean'].reshape(-1)
 s = np.sqrt(evaluation['covariance'].reshape(-1))
 plt.plot(x, evaluation['mean'].reshape(-1), label='evaluation', color='green')
 plt.fill_between(x, y-5*s,y+5*s, alpha=0.1, color='blue')
 plt.fill_between(x, y-3*s,y+3*s, alpha=0.1, color='blue')
 
 x = predictions['times'].reshape(-1)
 y = predictions['mean'].reshape(-1)
 s = np.sqrt(predictions['covariance'].reshape(-1))
 plt.plot(x, y, label='prediction',color='orange')
 plt.fill_between(x, y-5*s,y+5*s, alpha=0.1, color='orange')
 plt.fill_between(x, y-3*s,y+3*s, alpha=0.1, color='orange')
 
 plt.xlabel('time_step')
 plt.ylabel('values')
 _ = plt.legend()

Example image