1.数据简介 这个比赛是用大约14万5千个维基网页从2015年7月到2017年9月的访问量预测接下来的62天的访问量,其最大特点是用的实时数据,后面62天的结果比赛截止提交的时候谁也不知道,是后面出题方去维基爬的,不存…
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...
timeseries time-series tensorflow kaggle rnn seq2seq cudnn rnn-encoder-decoder kaggle-web-traffic cocob Updated Oct 9, 2022 Jupyter Notebook kairosdb / kairosdb Star 1.7k Code Issues Pull requests Fast scalable time series database java timeseries metrics kairosdb timeseries-database Update...
XGBoost is an open-source algorithm often used for many data science cases and in the Kaggle competition. Often the use cases are common classification cases such as fraud detection or regression cases such as house price prediction, but XGBoost can also be extended into time-series forecasting. ...
# Read train/test data and check colnames & NA's: original_train = pd.read_csv('/kaggle/input/store-sales-time-series-forecasting/train.csv') original_test = pd.read_csv('/kaggle/input/store-sales-time-series-forecasting/test.csv') ...
model= ARIMA(Train_log, order = (2,1,0))#here q value is zero since it is just AR Model SARIMAX Model,多元季节性时间序列模型,用于预测与异常诊断,参考博客:https://blog.csdn.net/weixin_41512727/article/details/82999831 importstatsmodels.api as sm ...
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。
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...
cv=PurgedGroupTimeSeriesSplit(n_splits=7,max_train_group_size=270,group_gap=0,max_test_group_size=30) 如果你只想做一个简单分割并尽可能扩大单个fold的训练数据量,可以将n_splits设置为2。(但不能设置为1,因为k-foldcross-validation至少是2折,如果只想分1折的话,那大可不必用这个库。) ...
多层感知机(MLP)、时间序列Transformer(time - series transformers)、截面注意力(cross-symbol attention)和嵌入(embeddings)对我来说效果不佳。 2. 响应变量(Responders): 我使用了 4 个响应变量作为辅助目标:responder_7 和 responder_8,以及两个计算得到的变量: df = df.with_columns( ( pl.col("responder_...