利用深度学习模型BiLSTM进行数据预测和分析

追蜻蜓追累了2024-07-27 16:13

导入必要的库和模块：

```python

import pandas as pd

from sklearn.model_selection import train_test_split

from sklearn.preprocessing import StandardScaler

from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score

import matplotlib.pyplot as plt

import numpy as np

import tensorflow as tf

from tensorflow.keras.models import Sequential

from tensorflow.keras.layers import Bidirectional, LSTM, Dense

```

加载数据并准备训练集和测试集：

```python

data = pd.read_excel('c.xlsx').iloc $0:, 1:$

X, y = data.iloc $:, 0:-1$ , data.iloc $:, -1$

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42, shuffle=False)

```

缩放特征：

```python

scaler = StandardScaler()

X_train_scaled = scaler.fit_transform(X_train)

X_test_scaled = scaler.transform(X_test)

```

构建 BiLSTM 模型并进行训练：

```python

model = Sequential()

model.add(Bidirectional(LSTM(units=64, return_sequences=True), input_shape=(X_train_scaled.shape $1$ , X_train_scaled.shape $2$ )))

model.add(Dense(1)) # Regression problems typically have output layer with one neuron

model.compile(loss='mean_squared_error', optimizer='adam')

model.fit(X_train_scaled, y_train, epochs=10, batch_size=32)

```

在测试集上进行预测并评估模型：

```python

y_pred = model.predict(X_test_scaled)

mse = mean_squared_error(y_test, y_pred)

rmse = np.sqrt(mse)

mae = mean_absolute_error(y_test, y_pred)

r2 = r2_score(y_test, y_pred)

print("RMSE:", rmse)

print("MAE:", mae)

print("R²:", r2)

```

绘制拟合对比曲线图：

```python

plt.figure(figsize=(10, 6))

plt.plot(range(len(y_test)), y_test, color='darkorange', label='Actual')

plt.plot(range(len(y_pred)), y_pred, color='navy', linewidth=2, label='Predicted')

plt.xlabel('Sample Index')

plt.ylabel('Target Variable')

plt.title('BiLSTM Regression Fit Comparison')

plt.legend()

plt.grid(True)

plt.show()

```