# Predict time series

import tensorflow as tf
import numpy as np

# Create time series
t_min, t_max = 0, 30
resolution = 0.1

def time_series(t):
    return t * np.sin(t) / 3 + 2 * np.sin(t * 5)

def next_batch(batch_size, n_steps):
    t0 = np.random.rand(batch_size, 1) * (t_max - t_min - n_steps * resolution)
    Ts = t0 + np.arange(0., n_steps + 1) * resolution
    ys = time_series(Ts)
    return ys[:,:-1].reshape(-1, n_steps, 1), ys[:,1:].reshape(-1, n_steps, 1)

t = np.linspace(t_min, t_max, (t_max - t_min) // resolution)

n_steps = 20
t_instance = np.linspace(12.2, 12.2 + resolution * (n_steps + 1), n_steps + 1)

n_inputs	=	1
n_neurons	=	100
n_outputs	=	1

X = tf.placeholder(tf.float32, [None, n_steps, n_inputs])
y = tf.placeholder(tf.float32, [None, n_steps, n_inputs])

# cell = tf.contrib.rnn.BasicRNNCell(num_units=n_neurons, activation=tf.nn.relu)

# Wrap cell in wrapper - want single output value at each step
cell = tf.contrib.rnn.OutputProjectionWrapper(
    tf.contrib.rnn.BasicRNNCell(num_units=n_neurons, activation=tf.nn.relu),
    output_size=n_outputs)
outputs, states = tf.nn.dynamic_rnn(cell, X, dtype=tf.float32, swap_memory=True)

learning_rate = 0.001

loss = tf.reduce_sum(tf.square(outputs - y))
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)
training_op = optimizer.minimize(loss)

init = tf.global_variables_initializer()

n_iterations = 1000
batch_size = 50

with tf.Session() as sess:
    init.run()
    for k in range(n_iterations):
        X_batch, y_batch = next_batch(batch_size, n_steps)
        sess.run(training_op, feed_dict={X: X_batch, y: y_batch})
        if k % 100 == 0:
            mse = loss.eval(feed_dict={X: X_batch, y: y_batch})
            print(k, "\tMSE: ", mse)

    X_new = time_series(np.array(t_instance[:-1].reshape(-1, n_steps, n_inputs)))
    y_pred = sess.run(outputs, feed_dict={X: X_new})
    print(y_pred)
Add code for RNN time series analysis
Add code for RNN time series analysis

Uses OutputProjectionWrapper
