# 技巧

在时序任务中，nn通过对特征的权重来拟合预测结果。如果时序数据中几乎没有类别变量的同质化数据，nn可能是第一选择。树模型本身是用split特征来预测，通过特征工程能更好的抓取复杂类别中蕴含的关系来做预测。

## 树模型

* 针对多步预测的几种方式

## 深度学习

**模块**

## 特征工程

* 时间特征
* diff特征，shift特征，roll特征

## 阅读

* [有了Transformer框架后是不是RNN完全可以废弃了？ - sonta的回答 - 知乎](https://www.zhihu.com/question/302392659/answer/2954997969)
* [Spatial-Temporal时间序列预测建模方法汇总](https://zhuanlan.zhihu.com/p/493349333)
* https://github.com/Arturus/kaggle-web-traffic/blob/master/how\_it\_works.md
* https://github.com/sjvasquez/web-traffic-forecasting
* https://github.com/jfpuget/Kaggle
* https://github.com/jerrywn121/TianChi\_AIEarth
* https://github.com/icodeworld/AI-competition/blob/main/tianchi-enso-prediction/RUN.py
* https://github.com/Wangjw6/Tianchi\_Prediction
* https://github.com/Phimos/SIGSPATIAL-2021-GISCUP-2nd-Place-Solution
* https://github.com/tstanczyk95/WeatherGCNet
* https://github.com/vishalsinghroha/Short-Term-Wind-Speed-Prediction-based-on-Deep-Learning/blob/master/main.py
* https://github.com/alexalex222/Wind-Speed-Prediction
* https://github.com/sharmamayank1741/Wind-Speed-Prediction-using-Time-Series-Analysis/blob/master/LSTM\_10\_daily.ipynb
* https://github.com/Wizaron/deep-forecast-pytorch/blob/master/lib/model.py
* https://github.com/LenzDu/Kaggle-Competition-Favorita
* https://github.com/jingw2/demand\_forecast
* https://github.com/zaburo-ch/kaggle-rrv-25th

## reference

验证 https://www.kaggle.com/code/sergeifironov/validate-ridge

embed + NN (早期NLP NN比赛)

transformer 参考

* https://github.com/nadavbra/protein\_bert/tree/master
* https://github.com/facebookresearch/esm
  * 使用transformers: https://www.kaggle.com/code/simonveitner/prepare-mean-embeddings
* https://github.com/Rostlab/goPredSim
* https://github.com/biomed-AI/SPROF-GO
  * https://www.kaggle.com/competitions/cafa-5-protein-function-prediction/discussion/417561
  * https://www.kaggle.com/code/samusram/blastp-sprof-go
* https://github.com/SamusRam/ProFun
* https://github.com/agemagician/ProtTrans
* https://github.com/Shen-Lab/TALE
* https://github.com/kexinhuang12345/DeepPurpose
* 多目标参考[kaggle-opm](https://www.kaggle.com/competitions/open-problems-multimodal)

不同的go term之间有图关系？ https://github.com/tanghaibao/goatools https://zhuanlan.zhihu.com/p/99789859 https://www.kaggle.com/competitions/foursquare-location-matching/discussion/336124

## 读物

https://zhuanlan.zhihu.com/p/571050734

关于测试集 https://www.kaggle.com/competitions/cafa-5-protein-function-prediction/discussion/403200

关于parent term https://www.kaggle.com/competitions/cafa-5-protein-function-prediction/discussion/403166

knn https://www.kaggle.com/code/geraseva/keops-knn

[Stationarity and Memory in Financial Markets](https://medium.com/data-science/non-stationarity-and-memory-in-financial-markets-fcef1fe76053)
