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介绍

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思路介绍：

具体代码：

import matplotlib.pyplot as pltimport numpy as npimport pandas as pdfrom keras import optimizersfrom keras.callbacks import EarlyStoppingfrom keras.layers import Input, Conv1D, MaxPooling1D, Dense, Dropout, Flattenfrom keras.models import Modelfrom sklearn.metrics import mean_absolute_errorfrom sklearn.preprocessing import MinMaxScalerimport osos.chdir(r'E:\项目文件\资金流入流出预测')user_balance = pd.read_csv('user_balance_table.csv')# user_profile = pd.read_csv('user_profile_table.csv')df_tmp = user_balance.groupby(['report_date'])['total_purchase_amt', 'total_redeem_amt'].sum()df_tmp.index = pd.to_datetime(df_tmp.index, format='%Y%m%d')holidays = ('20130813', '20130902', '20131001', '20131111', '20130919', '20131225', '20140101', '20140130', '20140131',            '20140214', '20140405', '20140501', '20140602', '20140802', '20140901', '20140908')def create_features(timeindex):    n = len(timeindex)    features = np.zeros((n, 4))    features[:, 0] = timeindex.day.values / 31    features[:, 1] = timeindex.month.values / 12    features[:, 2] = timeindex.weekday.values / 6    for i in range(n):        if timeindex[i].strftime('%Y%m%d') in holidays:            features[i, 3] = 1    return featuresfeatures = create_features(df_tmp.index)september = pd.to_datetime(['201409%02d' % i for i in range(1, 31)])features_sep = create_features(september)scaler_pur = MinMaxScaler()scaler_red = MinMaxScaler()data_pur = scaler_pur.fit_transform(df_tmp.values[:, 0:1])data_red = scaler_red.fit_transform(df_tmp.values[:, 1:2])def create_dataset(data, back, forward=30):    n_samples = len(data) - back - forward + 1    X, Y = np.zeros((n_samples, back, data.shape[-1])), np.zeros((n_samples, forward, data.shape[-1]))    for i in range(n_samples):        X[i, ...] = data[i:i + back, :]        Y[i, ...] = data[i + back:i + back + forward, :]    return X, Ydef build_cnn(X_trn, lr, n_outputs, dropout_rate):    inputs = Input(X_trn.shape[1:])    z = Conv1D(64, 14, padding='valid', activation='relu', kernel_initializer='he_uniform')(inputs)    #     z = MaxPooling1D(2)(z)    z = Conv1D(128, 7, padding='valid', activation='relu', kernel_initializer='he_uniform')(z)    z = MaxPooling1D(2)(z)    z = Conv1D(256, 3, padding='valid', activation='relu', kernel_initializer='he_uniform')(z)    z = Conv1D(256, 3, padding='valid', activation='relu', kernel_initializer='he_uniform')(z)    z = MaxPooling1D(2)(z)    z = Flatten()(z)    z = Dropout(dropout_rate)(z)    z = Dense(128, activation='relu', kernel_initializer='he_uniform')(z)    z = Dropout(dropout_rate)(z)    z = Dense(84, activation='relu', kernel_initializer='he_uniform')(z)    outputs = Dense(n_outputs)(z)    model = Model(inputs=inputs, outputs=outputs)    adam = optimizers.Adam(lr=lr)    model.compile(loss='mse', optimizer=adam, metrics=['mae'])    model.summary()    return modelback = 60forward = 30X_pur_data, Y_pur_data = create_dataset(data_pur, back, forward)X_red_data, Y_red_data = create_dataset(data_red, back, forward)X_features, Y_features = create_dataset(features, back, forward)Y_features = np.concatenate((Y_features, np.zeros((Y_features.shape[0], back-forward, Y_features.shape[-1]))), axis=1)# X_pur, X_red = np.concatenate((X_pur_data, X_features, Y_features), axis=-1), np.concatenate((X_red_data, X_features, Y_features), axis=-1)# X_pur_trn, X_pur_val, X_red_trn, X_red_val = X_pur[:-forward, ...], X_pur[-1:, ...], X_red[:-forward, ...], X_red[-1:, ...]# Y_pur_trn, Y_pur_val, Y_red_trn, Y_red_val = Y_pur_data[:-forward, ...], Y_pur_data[-1:, ...], Y_red_data[:-forward, ...], Y_red_data[-1:, ...]Y_fea_sep = np.concatenate((features_sep, np.zeros((back-forward, features_sep.shape[-1]))), axis=0)# X_pur_tst = np.concatenate((data_pur[-back:, :], features[-back:, :], Y_fea_sep), axis=-1)[None, ...]# X_red_tst = np.concatenate((data_red[-back:, :], features[-back:, :], Y_fea_sep), axis=-1)[None, ...]X = np.concatenate((X_pur_data, X_red_data, X_features, Y_features), axis=-1)Y = np.concatenate((Y_pur_data, Y_red_data), axis=1)X_trn, X_val, Y_trn, Y_val = X[:-forward, ...], X[-1:, ...], Y[:-forward, ...], Y[-1:, ...]X_tst = np.concatenate((data_pur[-back:, :], data_red[-back:, :], features[-back:, :], Y_fea_sep), axis=-1)[None, ...]cnn = build_cnn(X_trn, lr=0.0008, n_outputs=2 * forward, dropout_rate=0.5)history = cnn.fit(X_trn, Y_trn, batch_size=32, epochs=1000, verbose=2,                  validation_data=(X_val, Y_val),                  callbacks=[EarlyStopping(monitor='val_mae', patience=200, restore_best_weights=True)])plt.figure(figsize=(8, 5))plt.plot(history.history['mae'], label='train mae')plt.plot(history.history['val_mae'], label='validation mae')plt.ylim([0, 0.2])plt.legend()plt.show()def plot_prediction(y_pred, y_true):    plt.figure(figsize=(16, 4))    plt.plot(np.squeeze(y_pred), label='prediction')    plt.plot(np.squeeze(y_true), label='true')    plt.legend()    plt.show()    print('MAE: %.3f' % mean_absolute_error(np.squeeze(y_pred), np.squeeze(y_true)))pred = cnn.predict(X_val)plot_prediction(pred, Y_val)history = cnn.fit(X, Y, batch_size=32, epochs=500, verbose=2,                  callbacks=[EarlyStopping(monitor='mae', patience=30, restore_best_weights=True)])plt.figure(figsize=(8, 5))plt.plot(history.history['mae'], label='train mae')plt.legend()plt.show()print(cnn.evaluate(X, Y, verbose=2))pred_tst = cnn.predict(X_tst)pur_sep = scaler_pur.inverse_transform(pred_tst[:, :forward].transpose())red_sep = scaler_red.inverse_transform(pred_tst[:, forward:].transpose())test_user = pd.DataFrame({
   'report_date': [20140900 + i for i in range(1, 31)]})test_user['pur'] = pur_sep.astype('int')test_user['red'] = red_sep.astype('int')test_user.to_csv('submission.csv', encoding='utf-8', index=False, header=False)

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