Python - 使用Keras的LSTM回归神经网络进行模式预测

Question

我正在处理格式化CSV dataset的模式预测，其中包含三列（time_stamp，X和Y - 其中Y是实际值）。我想根据过去值的时间索引从Y预测X的值，这是我用Keras在Python中使用LSTM循环神经网络解决问题的方法。

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from keras.models import Sequential
from keras.layers import LSTM, Dense
from keras.preprocessing.sequence import TimeseriesGenerator
from sklearn.preprocessing import MinMaxScaler
from sklearn.model_selection import train_test_split

np.random.seed(7)

df = pd.read_csv('test32_C_data.csv')
n_features=100

values = df.values

for i in range(0,n_features):
    df['X_t'+str(i)] = df['X'].shift(i) 
    df['X_tp'+str(i)] = (df['X'].shift(i) - df['X'].shift(i+1))/(df['X'].shift(i))

print(df)
pd.set_option('use_inf_as_null', True)

#df.replace([np.inf, -np.inf], np.nan).dropna(axis=1)
df.dropna(inplace=True)

X = df.drop('Y', axis=1)
y = df['Y']


X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.40)

X_train = X_train.drop('time', axis=1)
X_train = X_train.drop('X_t1', axis=1)
X_train = X_train.drop('X_t2', axis=1)
X_test = X_test.drop('time', axis=1)
X_test = X_test.drop('X_t1', axis=1)
X_test = X_test.drop('X_t2', axis=1)


sc = MinMaxScaler()

X_train = np.array(df['X'])
X_train = X_train.reshape(-1, 1)
X_train = sc.fit_transform(X_train)

y_train = np.array(df['Y'])
y_train=y_train.reshape(-1, 1)
y_train = sc.fit_transform(y_train)


model_data = TimeseriesGenerator(X_train, y_train, 100, batch_size = 10) 

# Initialising the RNN
model = Sequential() 

# Adding the input layerand the LSTM layer
model.add(LSTM(4, input_shape=(None, 1)))

# Adding the output layer
model.add(Dense(1)) 

# Compiling the RNN
model.compile(loss='mse', optimizer='rmsprop') 

# Fitting the RNN to the Training set
model.fit_generator(model_data)

# evaluate the model
#scores = model.evaluate(X_train, y_train)
#print("\n%s: %.2f%%" % (model.metrics_names[1], scores[1]*100))


# Getting the predicted values
predicted = X_test
predicted = sc.transform(predicted)
predicted = predicted.reshape((-1, 1, 1))
y_pred = model.predict(predicted)
y_pred = sc.inverse_transform(y_pred)

当我将预测绘制为此

时

plt.figure
plt.plot(y_test, color = 'red', label = 'Actual')
plt.plot(y_pred, color = 'blue', label = 'Predicted')
plt.title('Prediction')
plt.xlabel('Time [INdex]')
plt.ylabel('Values')
plt.legend()
plt.show()

以下情节是我得到的。

但是，如果我们分别绘制每一列，

groups = [1, 2]
i = 1
# plot each column
plt.figure()
for group in groups:
    plt.subplot(len(groups), 1, i)
    plt.plot(values[:, group])
    plt.title(df.columns[group], y=0.5, loc='right')
    i += 1
plt.show()

下面的图是我们得到的。

我们如何提高预测准确度？

Answer 1

我允许你从这里拿走它，但至少应该让你去。

注意：我发现对于您预测的变量存在一些混淆。为此，我预测了“Y＆＃39;这通常是标准的。如果这不正确，只需在放入create_sequences函数之前交换订单。代码应该仍然有效，这对你来说只是一个起点，你需要更多地利用它来获得一个性能良好的网络。

import numpy as np
from keras.models import Sequential
from keras.layers import LSTM, Dense
from sklearn.preprocessing import MinMaxScaler
import pandas as pd

np.random.seed(7)

df = pd.read_csv('test32_C_data.csv')
n_features = 100


def create_sequences(data, window=14, step=1, prediction_distance=14):
    x = []
    y = []

    for i in range(0, len(data) - window - prediction_distance, step):
        x.append(data[i:i + window])
        y.append(data[i + window + prediction_distance][0])

    x, y = np.asarray(x), np.asarray(y)

    return x, y


# Scaling prior to splitting
scaler = MinMaxScaler(feature_range=(0.01, 0.99))
scaled_data = scaler.fit_transform(df.loc[:, ["Y", "X"]].values)

# Build sequences
x_sequence, y_sequence = create_sequences(scaled_data)

# Create test/train split
test_len = int(len(x_sequence) * 0.15)
valid_len = int(len(x_sequence) * 0.15)
train_end = len(x_sequence) - (test_len + valid_len)
x_train, y_train = x_sequence[:train_end], y_sequence[:train_end]
x_valid, y_valid = x_sequence[train_end:train_end + valid_len], y_sequence[train_end:train_end + valid_len]
x_test, y_test = x_sequence[train_end + valid_len:], y_sequence[train_end + valid_len:]

# Initialising the RNN
model = Sequential()

# Adding the input layerand the LSTM layer
model.add(LSTM(4, input_shape=(14, 2)))

# Adding the output layer
model.add(Dense(1))

# Compiling the RNN
model.compile(loss='mse', optimizer='rmsprop')

# Fitting the RNN to the Training set
model.fit(x_train, y_train, epochs=5)

# Getting the predicted values
y_pred = model.predict(x_test)

# Plot results
pd.DataFrame({"y_test": y_test, "y_pred": np.squeeze(y_pred)}).plot()

的差异：

• 使用自定义序列生成器，14步窗口，14步＆＃34;前瞻＆＃34;预测

• 自定义列车/测试/有效拆分，您可以在培训提前停止使用验证集

• 更改输入形状以包含2个功能，窗口为14 input_shape =（14,2）

• 5个时代