juanboy
48 lines
1.7 KiB
Python
48 lines
1.7 KiB
Python
import numpy as np
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import datetime
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import matplotlib.pyplot as plt
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def datetime_to_number(date: str): # 格式化日期转换为 integer
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date_number = datetime.datetime.strptime(date, "%Y-%m-%d")
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base_number = datetime.datetime.strptime("2024-1-1", "%Y-%m-%d")
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return (date_number - base_number).days
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def predict_future_values(data):
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# 提取并排序日期
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sorted_dates = sorted(data.keys(), key=lambda date: datetime.datetime.strptime(date, "%Y-%m-%d"))
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sorted_data = {k: data[k] for k in sorted_dates}
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# 将日期转换为整数并提取相应的值
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xs = np.array([datetime_to_number(date) for date in sorted_data.keys()])
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ys = np.array([data[date] for date in sorted_data.keys()])
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# 拟合线性回归模型
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fit = np.polyfit(xs, ys, 1)
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fn = np.poly1d(fit)
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# 获取最新日期，并生成未来三天的日期
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latest_date = sorted_dates[-1]
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latest_date_obj = datetime.datetime.strptime(latest_date, "%Y-%m-%d")
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future_dates = [(latest_date_obj + datetime.timedelta(days=i)).strftime("%Y-%m-%d") for i in range(1, 6)]
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# 预测未来日期的值
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predictions = {}
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for date in future_dates:
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date_num = datetime_to_number(date)
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if int(fn(date_num))<=0:
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predictions[date] = 0
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else:
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predictions[date] = int(fn(date_num))
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return predictions
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if __name__ == '__main__':
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data = {'2024-06-15': 1, '2024-06-18': 1, '2024-06-22': 1, '2024-06-23': 1, '2024-07-01': 3, '2024-07-02': 4, '2024-07-03': 4, '2024-07-04': 14}
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predictions = predict_future_values(data)
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print(predictions)
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# for date, value in predictions.items():
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# print(f'{date} PREDICTION: {value}')
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