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，将这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

好的，这里是基于Python的代码实现，需要安装pandas, numpy, matplotlib, tensorflow库：

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

# 读取数据
df = pd.read_excel(r"D:\xiangmu\utilits\name_of_your_excel_file3.xls", usecols=[4])
data = df.values.astype('float32')

# 数据归一化
scaler = MinMaxScaler(feature_range=(0, 1))
data = scaler.fit_transform(data)

# 划分训练集和测试集
train_size = 3000
test_size = len(data) - train_size
train, test = data[0:train_size,:], data[train_size:len(data),:]

# 将时间序列转化为监督学习问题
def create_dataset(dataset, look_back=1):
    X, Y = [], []
    for i in range(len(dataset)-look_back-1):
        a = dataset[i:(i+look_back), 0]
        X.append(a)
        Y.append(dataset[i + look_back, 0])
    return np.array(X), np.array(Y)

look_back = 1
X_train, Y_train = create_dataset(train, look_back)
X_test, Y_test = create_dataset(test, look_back)

# 将数据重构为 LSTM 的输入格式 [样本数，时间步，特征数]
X_train = np.reshape(X_train, (X_train.shape[0], 1, X_train.shape[1]))
X_test = np.reshape(X_test, (X_test.shape[0], 1, X_test.shape[1]))

# LSTM 模型
model = Sequential()
model.add(LSTM(units=50, input_shape=(1, look_back)))
model.add(Dense(units=1))
model.compile(loss='mean_squared_error', optimizer='adam')
model.fit(X_train, Y_train, epochs=100, batch_size=1, verbose=2)

# 模型预测
train_predict = model.predict(X_train)
test_predict = model.predict(X_test)

# 反归一化
train_predict = scaler.inverse_transform(train_predict)
Y_train = scaler.inverse_transform([Y_train])
test_predict = scaler.inverse_transform(test_predict)
Y_test = scaler.inverse_transform([Y_test])

# 计算 RMSE 评估模型
train_score = np.sqrt(np.mean(np.power(Y_train - train_predict, 2)))
test_score = np.sqrt(np.mean(np.power(Y_test - test_predict, 2)))
print('Train Score: %.2f RMSE' % (train_score))
print('Test Score: %.2f RMSE' % (test_score))

# 将预测结果输出到 Excel 文档
test_predict = np.vstack((np.full((train_size-look_back-1,1), np.nan), test_predict))
df['Predict'] = test_predict
df.to_excel(r"D:\xiangmu\utilits\name_of_your_excel_file3.xls", index=False)

这段代码将会将预测结果存储在 Excel 文件的第5列中，并将文件存储在指定路径下。