【发布时间】：2018-10-07 19:52:25
【问题描述】：
我尝试实现 LSTM 模型来进行时间序列预测。下面是我的试用代码。此代码运行没有错误。不依赖也可以试试。
import numpy as np, pandas as pd, matplotlib.pyplot as plt
from sklearn.preprocessing import MinMaxScaler
from keras.models import Sequential
from keras.layers import LSTM, Dense, TimeDistributed, Bidirectional
from sklearn.metrics import mean_squared_error, accuracy_score
from scipy.stats import linregress
from sklearn.utils import shuffle
fi = 'pollution.csv'
raw = pd.read_csv(fi, delimiter=',')
raw = raw.drop('Dates', axis=1)
print (raw.shape)
scaler = MinMaxScaler(feature_range=(-1, 1))
raw = scaler.fit_transform(raw)
time_steps = 7
def create_ds(data, t_steps):
data = pd.DataFrame(data)
data_s = data.copy()
for i in range(time_steps):
data = pd.concat([data, data_s.shift(-(i+1))], axis = 1)
data.dropna(axis=0, inplace=True)
return data.values
ds = create_ds(raw, time_steps)
print (ds.shape)
n_feats = raw.shape[1]
n_obs = time_steps * n_feats
n_rows = ds.shape[0]
train_size = int(n_rows * 0.8)
train_data = ds[:train_size, :]
train_data = shuffle(train_data)
test_data = ds[train_size:, :]
x_train = train_data[:, :n_obs]
y_train = train_data[:, n_obs:]
x_test = test_data[:, :n_obs]
y_test = test_data[:, n_obs:]
x_train = x_train.reshape(1, x_train.shape[0], x_train.shape[1])
y_train = y_train.reshape(1, y_train.shape[0], y_train.shape[1])
x_test = x_test.reshape(1, x_test.shape[0], x_test.shape[1])
print (x_train.shape)
print (y_train.shape)
print (x_test.shape)
print (y_test.shape)
model = Sequential()
model.add(LSTM(64, return_sequences=True, input_shape=(None, x_train.shape[2]), stateful=True, batch_size=1))
model.add(LSTM(32, return_sequences=True, stateful=True))
model.add(LSTM(n_feats, return_sequences=True, stateful=True))
model.compile(loss='mse', optimizer='rmsprop')
model.fit(x_train, y_train, epochs=10, batch_size=1, verbose=2)
y_predict = model.predict(x_test)
y_predict = y_predict.reshape(y_predict.shape[1], y_predict.shape[2])
y_predict = scaler.inverse_transform(y_predict)
y_test = scaler.inverse_transform(y_test)
y_test = y_test[:,0]
y_predict = y_predict[:,0]
print (y_test.shape)
print (y_predict.shape)
plt.plot(y_test, label='True')
plt.plot(y_predict, label='Predict')
plt.legend()
plt.show()
但是，预测极差。如何改进预测？你有什么改进它的想法吗？
有什么想法可以通过重新设计架构和/或层来改进预测？
【问题讨论】：
-
数据看起来很随机。也许这是 LSTM 在不过度拟合的情况下可以做到的最好的事情。一个好的经验法则是，如果您自己无法预测数据，则不应期望神经网络能够做到。
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这个预测看起来相当不错，实际上......除非有一些关于振荡周期的规则，那么你可以用更强大的模型来捕捉那个周期。但如果这个时期没有遵循任何模式，那么这是一个很好的预测。
标签： python tensorflow deep-learning keras keras-layer