多维度LSTM（长短期记忆）神经网络预测未来存款余额走势

多维度LSTM（长短期记忆）神经网络预测未来客户活期存款余额

''' Created on 2020年10月26日 @author: 寻找手艺人 '''
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from keras.models import Sequential  # Sequential 用于初始化神经网络
from keras.layers import Dense  # Dense 用于添加全连接的神经网络层
from keras.layers import LSTM  # LSTM 用于添加长短期内存层
from keras.layers import Dropout  # Dropout 用于添加防止过拟合的dropout层
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'



# 构建训练数据
def train_data():
    dataset_train = pd.read_excel('alive_data.xlsx', parse_dates=['date'])
    training_set = dataset_train.iloc[:, 1:7].values
    dataset_train.head()

    sc = MinMaxScaler(feature_range=(0, 1))
    training_set_scaled = sc.fit_transform(training_set)

    X_train = []
    y_train = []
    for i in range(60, len(training_set_scaled)):
        X_train.append(training_set_scaled[i - 60:i, :])
        y_train.append(training_set_scaled[i, :])

    X_train, y_train = np.array(X_train), np.array(y_train)
    X_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1], 6))

    return X_train, y_train, dataset_train, sc


# 构建测试数据
def test_data(dataset_train, sc):
    df = pd.read_excel('alive_data.xlsx', parse_dates=['date'])
    dataset_test = df[(df['date'] >= '2020-10-10')].loc[:,['first6', 'birthday', 'last4', 'gender', 'age', 'alive_amt']]
    real_stock_price = dataset_test['alive_amt'].values

    # 合并训练集和测试集
    dataset_total = pd.concat((dataset_train, dataset_test), axis=0) db_all = dataset_total.iloc[:, 1:7].values inputs = db_all[len(dataset_total) - len(dataset_test) - 60:] inputs = inputs.reshape(-1, dataset_test.shape[1]) inputs = sc.transform(inputs) X_test = [] for i in range(60, 60 + len(dataset_test)):
        X_test.append(inputs[i - 60:i, :])
        # X_test.append(inputs[i-60:i, 0])
    X_test = np.array(X_test)
    X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], dataset_test.shape[1]))

    return X_test, real_stock_price


# 创建股票预测模型
def stock_model(X_train, y_train):
    regressor = Sequential()
    # LSTM的输入为 [samples, timesteps, features],这里的timesteps为步数，features为维度 这里我们的数据是6维的
    regressor.add(LSTM(units=50, return_sequences=True, input_shape=(X_train.shape[1], 6)))

    regressor.add(Dropout(0.2))

    regressor.add(LSTM(units=50, return_sequences=True))
    regressor.add(Dropout(0.2))

    regressor.add(LSTM(units=50, return_sequences=True))
    regressor.add(Dropout(0.2))

    regressor.add(LSTM(units=50))
    regressor.add(Dropout(0.2))
    # 全连接，输出6个
    regressor.add(Dense(units=6))

    regressor.compile(optimizer='adam', loss='mean_squared_error')
    regressor.fit(X_train, y_train, epochs=1000, batch_size=32)

    return regressor


def main():
    X_train, y_train, dataset_train, sc = train_data()

    regressor = stock_model(X_train, y_train)
    X_test, real_stock_price = test_data(dataset_train, sc)
    predicted_stock_price = regressor.predict(X_test)

    predicted_stock_price = sc.inverse_transform(predicted_stock_price)

    plt.plot(real_stock_price, color='red', label='Real Alive amt')
    # 显示活期余额
    plt.plot(predicted_stock_price[:, 5], color='green', label='Predicted Alive amt')
    plt.title('Alive amt Prediction')
    plt.xlabel('Time')
    plt.ylabel('Alive amt Price')
    plt.legend()
    plt.show()


if __name__ == '__main__':
    main()

requirements.txt

absl-py==0.10.0
asgiref==3.2.10
astor==0.8.1
astunparse==1.6.3
beautifulsoup4==4.9.1
bs4==0.0.1
cachetools==4.1.1
certifi==2020.6.20
chardet==3.0.4
cycler==0.10.0
Django==3.1.1
django-bootstrap4==2.2.0
et-xmlfile==1.0.1
gast==0.2.2
google-auth==1.22.1
google-auth-oauthlib==0.4.1
google-pasta==0.2.0
grpcio==1.33.1
h5py==2.10.0
idna==2.10
importlib-metadata==1.7.0
jdcal==1.4.1
Jinja2==2.11.2
joblib==0.16.0
Keras==2.3.1
Keras-Applications==1.0.8
Keras-Preprocessing==1.1.2
kiwisolver==1.2.0
lxml==4.5.2
Markdown==3.3.2
MarkupSafe==1.1.1
matplotlib==3.3.2
mpl-finance==0.10.1
mplfinance==0.12.7a0
numpy==1.18.5
oauthlib==3.1.0
openpyxl==3.0.5
opt-einsum==3.3.0
pandas==1.1.3
Pillow==7.2.0
prettytable==0.7.2
protobuf==3.13.0
pyasn1==0.4.8
pyasn1-modules==0.2.8
pyecharts==1.8.1
PyMySQL==0.10.1
pyparsing==2.4.7
python-dateutil==2.8.1
pytz==2020.1
PyYAML==5.3.1
requests==2.24.0
requests-oauthlib==1.3.0
rsa==4.6
scikit-learn==0.23.2
scipy==1.4.1
seaborn==0.11.0
sequential==1.0.0
simplejson==3.17.2
six==1.15.0
soupsieve==2.0.1
sqlparse==0.3.1
tensorboard==2.0.2
tensorboard-plugin-wit==1.7.0
tensorflow==2.0.0
tensorflow-estimator==2.0.1
termcolor==1.1.0
threadpoolctl==2.1.0
tushare==1.2.61
urllib3==1.25.10
websocket-client==0.57.0
Werkzeug==1.0.1
wrapt==1.12.1
xlrd==1.2.0
zipp==3.1.0