ax2.set_title('Series 2') 图13 其中一个Series具有方程target = 0.95 * lag_1 + error,另一个具有方程target = -0.95 * lag_1 + error,仅在滞后特征上的符号不同。你能说出每个Series的方程式吗? Series 1 由 target = 0.95 * lag_1 + error 生成,Series 2 由 target = -0.95 * lag_1 + e...
Kaggle M5 Time Series Forecasting Competition | 实战案例 | 时间序列预测 Part 1:比赛介绍 338 -- 8:29 App Kaggle M5 Time Series Forecasting Competition | 实战案例 | 时间序列预测 Part 2:比赛介绍第二部分 359 -- 10:44 App Kaggle M5 Time Series Forecasting Competition | 实战案例 | 时间序列预测...
n_splits=9test_train_ratio=5len_timeids=len(df_train['time_id'].unique())max_test_group_size=int(len_timeids/(n_splits+test_train_ratio))max_train_group_size=max_test_group_size*test_train_ratiocv=PurgedGroupTimeSeriesSplit(n_splits=n_splits,max_train_group_size=max_train_group_...
Kaggle M5 Time Series Forecasting Competition | 实战案例 | 时间序列预测 Part 7:第一种ML方法 bilibibi土豆 328 0 Kaggle M5 Time Series Forecasting Competition | 实战案例 | 时间序列预测 Part 1:比赛介绍 bilibibi土豆 600 0 如何用Python对Kaggle测试集预测及提交 | Kaggle Titanic Dataset | Pandas ...
这个系列视频里我们会一起完成Kaggle的M5 Forecasting Competition。这是一个奖金$10,000目前正在进行的比赛,使用沃尔玛历史售货数据预测未来销量。 这是我的YouTube频道,可以登陆YouTube的朋友们如果喜欢可以点一波订阅支持一下:https://www.youtube.com/channel/UCgc9e-Ma04hZWogxvw2lp9g 视频会先在YouTube更新,...
Note that the feature must be a data frame, not a series. So usedf[['time']]instead ofdf.time. 1.4 Fit a lag feature to Store Sales Complete the code below to create a linear regression model with a lag feature on the series of average product sales. The target is in a column of...
今天才发现kaggle的Discussion和Kernel内容区别还挺大的。我原来一直在Kernel中找解决方案。其实很多都在Discussion版块给了自己解决方案描述并附加github。 Web Traffic Time Series Forcasting 该题目中提供了过去一年多时间的一些维基词语每天的访问情况,要求预测未来一年这些维基词语的访问情况。
Of course I can’t share production data, so I have picked up a public data set available in Kaggle for these first posts. The data set I am going to use is part of theAdvanced Retail Sales Time Series Collection, which is provided and maintained by theUnites States Census Bureau. There...
kaggle比赛有两种类型,一种是经典常见的,这样的比赛Notebook和论坛里有现成的baseline,大家都在相同的baseline上往上做,拼的的是细节。另一种是比较新颖的,没有现成的baseline,这样的比赛拼的就是谁的baseline好了。而wtf这个比赛之前kaggle很长时间没有时序比赛,再加上树模型意外失效,因此最后的top solution可谓是...
fastiotsqldatabasetimeseriestime-seriesnosqlbigdatanewsqlgriddb UpdatedNov 13, 2024 C++ 1st place solution timeseriestime-seriestensorflowkagglernnseq2seqcudnnrnn-encoder-decoderkaggle-web-trafficcocob UpdatedOct 9, 2022 Jupyter Notebook Fast scalable time series database ...