基于ARIMA时间序列的销量预测模型，实际预测准确率达90%以上，内含有测试记录和实际上线效果。
ARIMA模型提供了基于时间序列理论，对数据进行平稳化处理（AR和MA过程）、模型定阶（自动差分过程）、参数估计，建立模型，并对模型进行检验。 在Python中statsmodel提供了全套的解决方案，包括窗口选择、自动定阶和平稳性检测等等算法。
每月分上中下旬三个点预测，每月预测三次当月销量。这么做的好处是，月上旬和中旬的实际销量可以作为先验知识，提高模型预测的准确率。
pip install -r requirements.txt
python sales.py
截止到8月15日累计销量486，预测8月份销量为889
如结果集1,2,3,4,5时程序报错
嗨 你好作者 看了你的时间序列预测的代码对我启发很大
但是有一些问题想要请教您 很着急 可以加我个微信么 有偿的 谢谢您 非常感谢您 17343143306
算法中如何添加节假日及促销的因素呢
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