给出时间序列预测adnn方法的python代码

ADNN（Adaptive Deep Neural Network）是一种基于深度学习的时间序列预测方法，下面是一个简单的Python代码示例：

首先，导入必要的库：

import numpy as np
import pandas as pd
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, LSTM, Dropout
from tensorflow.keras.callbacks import EarlyStopping

然后，定义一个函数来准备时间序列数据：

def prepare_data(timeseries, n_features):
    X, Y = [], []
    for i in range(len(timeseries)):
        # 获取当前时间步的输入和输出
        end_ix = i + n_features
        if end_ix > len(timeseries)-1:
            break
        seq_x, seq_y = timeseries[i:end_ix], timeseries[end_ix]
        X.append(seq_x)
        Y.append(seq_y)
    return np.array(X), np.array(Y)

接下来，读取并准备时间序列数据：

df = pd.read_csv('timeseries.csv', header=None)
timeseries = df.values.flatten()
n_features = 3
X, Y = prepare_data(timeseries, n_features)

然后，定义一个ADNN模型：

model = Sequential()
model.add(LSTM(50, activation='relu', input_shape=(n_features, 1)))
model.add(Dropout(0.2))
model.add(Dense(1))
model.compile(optimizer='adam', loss='mse')

接下来，训练ADNN模型：

model.fit(X.reshape((X.shape[0], X.shape[1], 1)), Y, epochs=100, verbose=0, callbacks=[EarlyStopping(monitor='val_loss', patience=20)], validation_split=0.2)

最后，使用ADNN模型进行预测：

# 预测最近3个时间步的数据
test_input = np.array([timeseries[-3:], timeseries[-2:], timeseries[-1:]])
test_input = test_input.reshape((1, n_features, 1))
test_output = model.predict(test_input, verbose=0)
print(test_output)