时序预测一直是比较重要的研究问题，在统计学中我们有各种的模型来解决时间序列问题，但是最近几年比较火的深度学习中也有能解决时序预测问题的方法，另外在深度学习领域中时序预测算法可以解决自然语言问题等。
在网上找到了 tensorflow 中 RNN 和 LSTM 算法预测 sin 曲线的代码，效果不错。
LSTM:
#encoding:UTF-8
import random
import numpy as np
import tensorflow as tf
from tensorflow.contrib.rnn.python.ops import core_rnn
from tensorflow.contrib.rnn.python.ops import core_rnn_cell
def build_data(n):
xs = []
ys = []
for i in range(0, 2000):
k = random.uniform(1, 50)
x = [[np.sin(k + j)] for j in range(0, n)]
y = [np.sin(k + n)]
# x[i] = sin(k + i) (i = 0, 1, ..., n-1)
# y[i] = sin(k + n)
xs.append(x)
ys.append(y)
train_x = np.array(xs[0: 1500])
train_y = np.array(ys[0: 1500])
test_x = np.array(xs[1500:])
test_y = np.array(ys[1500:])
return (train_x, train_y, test_x, test_y)
length = 10
time_step_size = length
vector_size = 1
batch_size = 10
test_size = 10
# build data
(train_x, train_y, test_x, test_y) = build_data(length)
print(train_x.shape, train_y.shape, test_x.shape, test_y.shape)
X = tf.placeholder("float", [None, length, vector_size])
Y = tf.placeholder("float", [None, 1])
# get lstm_size and output predicted value
W = tf.Variable(tf.random_normal([10, 1], stddev=0.01))
B = tf.Variable(tf.random_normal([1], stddev=0.01))
def seq_predict_model(X, w, b, time_step_size, vector_size):
# input X shape: [batch_size, time_step_size, vector_size]
# transpose X to [time_step_size, batch_size, vector_size]
X = tf.transpose(X, [1, 0, 2])
# reshape X to [time_step_size * batch_size, vector_size]
X = tf.reshape(X, [-1, vector_size])
# split X, array[time_step_size], shape: [batch_size, vector_size]
X = tf.split(X, time_step_size, 0)
# LSTM model with state_size = 10
cell = core_rnn_cell.BasicLSTMCell(num_units=10,
forget_bias=1.0,
state_is_tuple=True)
outputs, _states = core_rnn.static_rnn(cell, X, dtype=tf.float32)
# Linear activation
return tf.matmul(outputs[-1], w) + b, cell.state_size
pred_y, _ = seq_predict_model(X, W, B, time_step_size, vector_size)
loss = tf.square(tf.subtract(Y, pred_y))
train_op = tf.train.GradientDescentOptimizer(0.001).minimize(loss)
with tf.Session() as sess:
tf.global_variables_initializer().run()
# train
for i in range(50):
# train
for end in range(batch_size, len(train_x), batch_size):
begin = end - batch_size
x_value = train_x[begin: end]
y_value = train_y[begin: end]
sess.run(train_op, feed_dict={X: x_value, Y: y_value})
# randomly select validation set from test set
test_indices = np.arange(len(test_x))
np.random.shuffle(test_indices)
test_indices = test_indices[0: test_size]
x_value = test_x[test_indices]
y_value = test_y[test_indices]
# eval in validation set
val_loss = np.mean(sess.run(loss,
feed_dict={X: x_value, Y: y_value}))
print('Run %s' % i, val_loss)
for b in range(0, len(test_x), test_size):
x_value = test_x[b: b + test_size]
y_value = test_y[b: b + test_size]
pred = sess.run(pred_y, feed_dict={X: x_value})
for i in range(len(pred)):
print(pred[i], y_value[i], pred[i] - y_value[i])
RNN :
import random
import numpy as np
import tensorflow as tf
from tensorflow.contrib.rnn.python.ops import core_rnn
from tensorflow.contrib.rnn.python.ops import core_rnn_cell
def build_data(n):
xs = []
ys = []
for i in range(0, 2000):
k = random.uniform(1, 50)
x = [[np.sin(k + j)] for j in range(0, n)]
y = [np.sin(k + n)]
# x[i] = sin(k + i) (i = 0, 1, ..., n-1)
# y[i] = sin(k + n)
xs.append(x)
ys.append(y)
train_x = np.array(xs[0: 1500])
train_y = np.array(ys[0: 1500])
test_x = np.array(xs[1500:])
test_y = np.array(ys[1500:])
return (train_x, train_y, test_x, test_y)
length = 10
time_step_size = length
vector_size = 1
batch_size = 10
test_size = 10
# build data
(train_x, train_y, test_x, test_y) = build_data(length)
print(train_x.shape, train_y.shape, test_x.shape, test_y.shape)
X = tf.placeholder("float", [None, length, vector_size])
Y = tf.placeholder("float", [None, 1])
# get lstm_size and output predicted value
W = tf.Variable(tf.random_normal([10, 1], stddev=0.01))
B = tf.Variable(tf.random_normal([1], stddev=0.01))
def seq_predict_model(X, w, b, time_step_size, vector_size):
# input X shape: [batch_size, time_step_size, vector_size]
# transpose X to [time_step_size, batch_size, vector_size]
X = tf.transpose(X, [1, 0, 2])
# reshape X to [time_step_size * batch_size, vector_size]
X = tf.reshape(X, [-1, vector_size])
# split X, array[time_step_size], shape: [batch_size, vector_size]
X = tf.split(X, time_step_size, 0)
cell = core_rnn_cell.BasicRNNCell(num_units=10)
initial_state = tf.zeros([batch_size, cell.state_size])
outputs, _states = core_rnn.static_rnn(cell, X, initial_state=initial_state)
# Linear activation
return tf.matmul(outputs[-1], w) + b, cell.state_size
pred_y, _ = seq_predict_model(X, W, B, time_step_size, vector_size)
loss = tf.square(tf.subtract(Y, pred_y))
train_op = tf.train.GradientDescentOptimizer(0.001).minimize(loss)
with tf.Session() as sess:
tf.global_variables_initializer().run()
# train
for i in range(50):
# train
for end in range(batch_size, len(train_x), batch_size):
begin = end - batch_size
x_value = train_x[begin: end]
y_value = train_y[begin: end]
sess.run(train_op, feed_dict={X: x_value, Y: y_value})
# randomly select validation set from test set
test_indices = np.arange(len(test_x))
np.random.shuffle(test_indices)
test_indices = test_indices[0: test_size]
x_value = test_x[test_indices]
y_value = test_y[test_indices]
# eval in validation set
val_loss = np.mean(sess.run(loss,
feed_dict={X: x_value, Y: y_value}))
print('Run %s' % i, val_loss)
for b in range(0, len(test_x), test_size):
x_value = test_x[b: b + test_size]
y_value = test_y[b: b + test_size]
pred = sess.run(pred_y, feed_dict={X: x_value})
for i in range(len(pred)):
print(pred[i], y_value[i], pred[i] - y_value[i])
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