请编写代码,利用lstm模型进行时间序列预测,使其仅对序列进行单步预测,随后通过拟合窗口与预测窗口随时间向右滑动,得到关于该序列的完整预测序列,其中加载数据时读取时间序列为r"D:\xiangmu\utilits\name_of_your_excel_file3.xls"文件路径中第5列前3000行的值,将3000个数据分为训练集与测试集,训练模型后,利用模型预测第3000-3500行的数据,并将预测结果输出到excel文档中
时间: 2024-02-16 14:04:09 浏览: 80
好的,根据您的要求,我为您编写了以下代码:
```python
import pandas as pd
import numpy as np
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import LSTM, Dense
from sklearn.preprocessing import MinMaxScaler
# 读取数据
data = pd.read_excel(r"D:\xiangmu\utilits\name_of_your_excel_file3.xls", usecols=[4]).values
data = data[:3000]
# 将数据标准化
scaler = MinMaxScaler(feature_range=(0, 1))
data = scaler.fit_transform(data)
# 划分训练集和测试集
train_size = int(len(data) * 0.8)
train_data, test_data = data[:train_size], data[train_size:]
# 将数据转化为有监督学习形式
def create_dataset(data, look_back=1):
X, Y = [], []
for i in range(len(data)-look_back):
X.append(data[i:i+look_back, 0])
Y.append(data[i+look_back, 0])
return np.array(X), np.array(Y)
look_back = 1
train_X, train_Y = create_dataset(train_data, look_back)
test_X, test_Y = create_dataset(test_data, look_back)
# 转化为3D形式,(样本数, 时间步数, 特征数)
train_X = np.reshape(train_X, (train_X.shape[0], 1, train_X.shape[1]))
test_X = np.reshape(test_X, (test_X.shape[0], 1, test_X.shape[1]))
# 构建模型
model = Sequential()
model.add(LSTM(50, input_shape=(1, look_back)))
model.add(Dense(1))
model.compile(loss='mean_squared_error', optimizer='adam')
# 训练模型
model.fit(train_X, train_Y, epochs=50, batch_size=64, verbose=2)
# 预测测试集
test_predict = model.predict(test_X)
test_predict = scaler.inverse_transform(test_predict)
test_Y = scaler.inverse_transform([test_Y])
# 单步预测3000-3500行的数据
last_data = data[-look_back:]
for i in range(500):
next_data = model.predict(last_data.reshape(1, 1, look_back))
data = np.append(data, next_data)
last_data = data[-look_back:]
# 将预测结果输出到excel文档中
df = pd.DataFrame(data[-500:], columns=['predicted'])
df.to_excel(r"D:\xiangmu\utilits\predicted_results.xls", index=False)
```
请注意,您需要将代码中的文件路径替换为您自己的文件路径,并且确保已经安装了所需的库。此外,该模型的预测能力可能受到多种因素的影响,如数据质量、模型参数等,因此预测结果仅供参考。
阅读全文