LSTM预测值为何是一条直线

Question

LSTM建模为何训练值为一条直线

import pandas as pd
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt

df=pd.read_excel('分钟数据集.xlsx',index_col='日期',engine='openpyxl')

#归一化：
data=df
data_max = data.max()
data_min = data.min()
data = (data-data_min)/(data_max-data_min)

dataset_st = np.array(data)

def data_set(dataset, lookback): # 创建时间序列数据样本

dataX, dataY = [], []

for i in range(len(dataset) - lookback):
    a = dataset[i:(i + lookback)]

    dataX.append(a[1:])

    dataY.append(dataset[i + lookback][0])

return np.array(dataX), np.array(dataY)

划分训练集和测试集

train_size = int(len(dataset_st) * 0.7)

test_size = len(dataset_st) - train_size

train, test = dataset_st[0:train_size], dataset_st[train_size:len(dataset_st)]

print(len(train))

print(len(test))

根据划分的训练集测试集生成需要的时间序列样本数据

lookback = 240

trainX, trainY = data_set(train, lookback)

testX, testY = data_set(test, lookback)

#LSTM回归预测 建模
#步骤 1 初始化神经网络:

model=tf.keras.Sequential([tf.keras.layers.LSTM(60,input_shape=(trainX.shape[1],

trainX.shape[2]),return_sequences=True),

tf.keras.layers.Dropout(0.2),

tf.keras.layers.LSTM(120),

tf.keras.layers.Dropout(0.2),

tf.keras.layers.Dense(30,activation='sigmoid'),

tf.keras.layers.Dense(1)

])

#步骤 2 定义学习率更新规则:
model.compile(

optimizer=tf.keras.optimizers.Adam(0.0001), #学习率

loss='mean_squared_error',  # 损失函数用交叉熵

metrics=["mse"]

)

#步骤 3 构建模型训练:
history = model.fit(trainX, trainY,

                batch_size=40, epochs=5, validation_data=(testX, testY),

                validation_freq=1)

#步骤 4 构建绘图函数:
def plot_learning_curves(history):

pd.DataFrame(history.history).plot(figsize=(10,6))

plt.grid(True)

plt.title('训练情况')

plt.savefig('./训练情况.jpg')

plt.show()

#模型验证:
test_preds=model.predict(testX,verbose=1)

plt.figure(figsize=(16,8))
plt.plot(testY[:300], label="True value")
plt.plot(test_preds[:300], label="Pred value")
plt.legend(loc='best')
plt.show()

运行结果：

Epoch 1/5
3073/3073- 503s 163ms/step - loss: 0.0175 - mse: 0.0175 - val_loss: 7.0646e-05 - val_mse: 7.0646e-05
Epoch 2/5
3073/3073 - 493s 161ms/step - loss: 1.6900e-04 - mse: 1.6900e-04 - val_loss: 5.8696e-05 - val_mse: 5.8696e-05
Epoch 3/5
3073/3073 - 540s 176ms/step - loss: 1.2051e-04 - mse: 1.2051e-04 - val_loss: 5.8187e-05 - val_mse: 5.8187e-05
Epoch 4/5
3073/3073- 479s 156ms/step - loss: 1.0361e-04 - mse: 1.0361e-04 - val_loss: 5.7863e-05 - val_mse: 5.7863e-05
Epoch 5/5
3073/3073 - 512s 167ms/step - loss: 9.4500e-05 - mse: 9.4500e-05 - val_loss: 6.6830e-05 - val_mse: 6.6830e-05
1642/1642 - 69s 42ms/step

预测值是一条直线

求问是什么原因导致的啊，样本已经进行了差分处理

Answer 1

你这time step这么大，而训练集长度就比这个大一些，训练出来的模型进行预测会出现结果波动不大，呈一条直线的情况。