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分享一篇很有趣的 文章。用梯度提升的方法来改进 ARIMA,AutoARIMA 先拿到起始参数,Boosting 过程和普通 GBM 一样都是用残差代替梯度计算。角度倒是听新颖的, 就是听着可能速度有点慢,而且不能确定序列角度来说 Boosting 是否真的有意义,作者在 Kaggle 去年的 M4 上做了些实验,看着还行,有点意思。轮子在这里:https://github.com/tblume1992/ThymeBoost



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分享一篇很有趣的 文章。用梯度提升的方法来改进 ARIMA,AutoARIMA 先拿到起始参数,Boosting 过程和普通 GBM 一样都是用残差代替梯度计算。角度倒是听新颖的, 就是听着可能速度有点慢,而且不能确定序列角度来说 Boosting 是否真的有意义,作者在 Kaggle 去年的 M4 上做了些实验,看着还行,有点意思。轮子在这里:https://github.com/tblume1992/ThymeBoost

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Importantly, that investor viewpoint is not new. It cycles in when conditions are right (and vice versa). It also brings the ineffective warnings of an overpriced market with it.Looking toward a good 2022 stock market, there is no apparent reason to expect these issues to change.

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