用python进行LSTM预测
时间: 2023-11-16 22:07:07 浏览: 35
好的,下面是一个用Python进行LSTM预测的示例:
首先,需要导入必要的库和模块,包括numpy、pandas、matplotlib和sklearn等:
```python
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.preprocessing import MinMaxScaler
from keras.models import Sequential
from keras.layers import Dense, LSTM
```
接下来,读取数据集并进行预处理,包括数据归一化、数据转换、数据切分等:
```python
# 读取数据集
df = pd.read_csv('dataset.csv')
# 取出需要预测的列
data = df.filter(['target'])
# 将数据集转换为numpy数组
dataset = data.values
# 获取训练集的长度
training_data_len = int(np.ceil(len(dataset) * 0.8))
# 对数据进行归一化
scaler = MinMaxScaler(feature_range=(0, 1))
scaled_data = scaler.fit_transform(dataset)
# 将数据集切分为训练集和测试集
train_data = scaled_data[0:training_data_len, :]
x_train = []
y_train = []
for i in range(60, len(train_data)):
x_train.append(train_data[i-60:i, 0])
y_train.append(train_data[i, 0])
test_data = scaled_data[training_data_len-60:, :]
x_test = []
y_test = dataset[training_data_len:, :]
for i in range(60, len(test_data)):
x_test.append(test_data[i-60:i, 0])
```
然后,构建LSTM模型并进行训练:
```python
# 将训练集和测试集转换为numpy数组
x_train, y_train = np.array(x_train), np.array(y_train)
x_test = np.array(x_test)
# 将训练集重塑为LSTM模型所需的格式
x_train = np.reshape(x_train, (x_train.shape[0], x_train.shape[1], 1))
# 构建LSTM模型
model = Sequential()
model.add(LSTM(50, return_sequences=True, input_shape=(x_train.shape[1], 1)))
model.add(LSTM(50, return_sequences=False))
model.add(Dense(25))
model.add(Dense(1))
# 编译模型
model.compile(optimizer='adam', loss='mean_squared_error')
# 训练模型
model.fit(x_train, y_train, batch_size=1, epochs=1)
```
最后,使用训练好的模型进行预测并进行可视化:
```python
# 获取模型预测值
predictions = model.predict(x_test)
predictions = scaler.inverse_transform(predictions)
# 计算均方根误差
rmse = np.sqrt(np.mean((predictions - y_test) ** 2))
# 可视化预测结果
train = data[:training_data_len]
valid = data[training_data_len:]
valid['Predictions'] = predictions
plt.figure(figsize=(16,8))
plt.title('LSTM Model')
plt.xlabel('Date', fontsize=18)
plt.ylabel('Price', fontsize=18)
plt.plot(train['target'])
plt.plot(valid[['target', 'Predictions']])
plt.legend(['Train', 'Validation', 'Predictions'], loc='lower right')
plt.show()
```
这就是一个使用Python进行LSTM预测的完整示例。