现金流月度预测的python代码

原创

wx66598883247d1 2024-06-05 10:16:05 ©著作权

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解释说明

现金流月度预测是一个典型的时间序列预测问题，我们可以使用LIGHTGBM模型来解决。LIGHTGBM是一种基于梯度提升决策树（Gradient Boosting Decision Tree）的高效机器学习算法，它具有较高的预测准确性和较快的训练速度。在这个问题中，我们将使用LIGHTGBM模型来预测每个月的现金流。

pip install lightgbm
import numpy as np
import pandas as pd
import lightgbm as lgb
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error

# 加载数据
data = pd.read_csv('cash_flow_data.csv')

# 数据预处理
data['date'] = pd.to_datetime(data['date'])
data['month'] = data['date'].dt.month
data['year'] = data['date'].dt.year
data['day_of_year'] = data['date'].dt.dayofyear

# 特征工程
features = ['month', 'year', 'day_of_year']
target = 'cash_flow'

X = data[features]
y = data[target]

# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# 创建LIGHTGBM数据集
train_data = lgb.Dataset(X_train, label=y_train)
test_data = lgb.Dataset(X_test, label=y_test)

# 设置LIGHTGBM参数
params = {
    'boosting_type': 'gbdt',
    'objective': 'regression',
    'metric': 'rmse',
    'num_leaves': 31,
    'learning_rate': 0.05,
    'feature_fraction': 0.9,
    'bagging_fraction': 0.8,
    'bagging_freq': 5,
    'verbose': 0
}

# 训练模型
gbm = lgb.train(params,
                train_data,
                num_boost_round=100,
                valid_sets=test_data,
                early_stopping_rounds=10)

# 预测
y_pred = gbm.predict(X_test, num_iteration=gbm.best_iteration)

# 计算误差
rmse = np.sqrt(mean_squared_error(y_test, y_pred))
print('RMSE:', rmse)