提供EEMD+ConvLSTM的读取csv数据,前80%为训练集,后20%为测试集的多输入单输出的时序训练及预测代码
时间: 2023-03-06 08:22:04 浏览: 96
以下是一个基本的EEMD ConvLSTM模型的读取csv数据,前80%为训练集,后20%为测试集的多输入单输出的时序训练及预测代码。需要注意的是,该代码仅提供了一个框架,需要根据具体数据进行修改。
```python
# 导入所需库
import pandas as pd
import numpy as np
from sklearn.preprocessing import MinMaxScaler
from sklearn.metrics import mean_squared_error
from tensorflow.keras.models import Sequential, Model
from tensorflow.keras.layers import Dense, LSTM, ConvLSTM2D, Flatten, Dropout, Input, concatenate
from PyEMD import EEMD
# 读取CSV数据
data = pd.read_csv('your_csv_file.csv')
# 数据预处理
scaler = MinMaxScaler(feature_range=(0, 1))
data_scaled = scaler.fit_transform(data)
# 定义函数将数据划分为训练集和测试集
def split_data(data, n_steps):
X, y = [], []
for i in range(len(data)):
end_ix = i + n_steps
if end_ix > len(data)-1:
break
seq_x, seq_y = data[i:end_ix, :-1], data[end_ix, -1]
X.append(seq_x)
y.append(seq_y)
return np.array(X), np.array(y)
# 定义模型参数
n_steps = 3
n_features = 3
n_seq = 2
n_seq2 = 2
n_filters = 64
kernel_size = (1,2)
dropout = 0.2
# 分割数据为训练集和测试集
train_size = int(len(data_scaled) * 0.8)
test_size = len(data_scaled) - train_size
train, test = data_scaled[0:train_size,:], data_scaled[train_size:len(data_scaled),:]
# 将数据划分为多个输入和单个输出
train_X, train_y = split_data(train, n_steps)
test_X, test_y = split_data(test, n_steps)
# EEMD预处理
eemd = EEMD()
train_X_eemd = np.zeros_like(train_X)
test_X_eemd = np.zeros_like(test_X)
for i in range(len(train_X)):
for j in range(n_features):
train_X_eemd[i,:,j] = eemd.eemd(train_X[i,:,j], max_imf=2)
for i in range(len(test_X)):
for j in range(n_features):
test_X_eemd[i,:,j] = eemd.eemd(test_X[i,:,j], max_imf=2)
# 将数据重塑为ConvLSTM2D期望的格式
train_X_eemd = train_X_eemd.reshape((train_X_eemd.shape[0], n_seq, 1, n_steps, n_features))
test_X_eemd = test_X_eemd.reshape((test_X_eemd.shape[0], n_seq, 1, n_steps, n_features))
# 构建EEMD ConvLSTM2D模型
inputs = Input(shape=(n_seq, 1, n_steps, n_features))
convLstm1 = ConvLSTM2D(filters=n_filters, kernel_size=kernel_size, activation='relu', dropout=dropout, padding='same', return_sequences=True)(inputs)
convLstm2 = ConvLSTM2D(filters=n_filters, kernel_size=kernel_size, activation='