1.预测商品销量比赛
代码1：使用LSTM
https://www.kaggle.com/code/sanjaylalwani/lstm-predict-sales
代码2：传统方法AR, MA and ARMA models
https://www.kaggle.com/code/jagangupta/time-series-basics-exploring-traditional-ts
代码3：Feature engineering+xgboost
https://www.kaggle.com/code/dlarionov/feature-engineering-xgboost
代码4：Model stacking, feature engineering and EDA
https://www.kaggle.com/code/dimitreoliveira/model-stacking-feature-engineering-and-eda
2.餐厅人流预测
1.特征工程+lgb
https://www.kaggle.com/code/plantsgo/solution-public-0-471-private-0-505/script
2.ARIMA models
https://www.kaggle.com/code/timolee/feeling-hungry-a-beginner-s-guide-to-arima-models
3.EDA
https://www.kaggle.com/code/headsortails/be-my-guest-recruit-restaurant-eda
4.keras
kaggle.com/code/nitinsurya/surprise-me-2-neural-networks-keras
5.LSTM
https://www.kaggle.com/code/yekenot/explore-ts-with-lstm
3.Store Sales - Time Series Forecasting
1.Lstm
https://www.kaggle.com/code/minhajulhoque/deep-learning-multivariate-rnn-lstm-network
2.ARIMA
https://www.kaggle.com/code/ceruttivini/store-sales-forecasting-arima-and-autoarima
3.EDA + XGB
https://www.kaggle.com/code/satoshiss/store-sales-eda-xgb