License: MIT License
对ARIMA和LSTM模型的一个简单融合
数据：天池大赛的余额宝数据
思路：
1.对数据进行季节分量的加性分解，得到趋势分量、季节分量、残差分量
2.对趋势曲线和残差曲线进行平稳性检验，根据ACF指标和PACF指标、季节分量的周期长度确定马尔科夫链的长度（取最大的）
3.将趋势曲线、残差曲线和天气、节假日等协变量数据拼接导入LSTM层
4.LSTM层的输出与季节性分量拼接导入MLPdecoder得到最终结果
预测模式：由于季节性分量的周期长度为7天，比两个曲线的ACF、PACF指标要大，因此，马尔科夫链的长度定为7，预测步长为1
训练集：2014年4月1日 - 2014年8月1日
测试集：2014年8月2日 - 2014年8月31日
效果：测试集上的22个样本的最低百分比误差为0.35%,最高百分比误差为6.72%，总百分比误差为0.64%
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