数理统计｜回归分析的概念
变量之间的关系可以分为两类
- 确定性关系
- 非确定性关系
若有变量x和y, 当变量x的值确定之后，变量y的值也随之确定，这种变量之间的关系是确定性关系。
变量x和Y是有联系的，但是当x的值确定时Y的值却是不确定的。这种变量间的关系就是非确定性的关系，也称为相关关系。
研究变量间相关关系的统计分析方法称为回归分析。
x通常称为自变量，Y通常成为因变量或响应变量。
当自变量x的值确定之后，因变量Y的值还不能完全确定，把Y看作随机变量。当x的值确定时，与x相对应的随机变量Y的值虽然不能完全确定，但Y的数学期望随之确定，这个数学期望应是x的函数，记作μ(x) , 称为Y关于x的回归函数。
Y=μ（x）+ ε(随机误差)
dataset = tfio.experimental.IODataset.from_prometheus(
"coredns_dns_request_count_total", 5, endpoint="http://172.16.59.21:9090")
print("Dataset Spec:\n{}\n".format(dataset.element_spec))
print("CoreDNS Time Series:")
for (time, value) in dataset:
# time is milli second, convert to data time:
#time = datetime.fromtimestamp(time // 1000)
print("{}: {}".format(time, value['coredns']['localhost:9153']['coredns_dns_request_count_total']))Dataset Spec:
(TensorSpec(shape=(), dtype=tf.int64, name=None), {'coredns': {'localhost:9153': {'coredns_dns_request_count_total': TensorSpec(shape=(), dtype=tf.float64, name=None)}}})
CoreDNS Time Series:
8317695948674261788: 2.5e-323
2305843009213693952: 8.11115665425e-312
2305843009213693957: 1.4916681462400413e-154
2305843009213693952: 8.111157181676e-312https://www.tensorflow.org/io/tutorials/prometheus
https://www.tabnine.com/blog/top-jupyterlab-extensions/?utm_term=&utm_campaign=&utm_source=adwords&utm_medium=ppc&hsa_acc=4311736126&hsa_cam=14854202152&hsa_grp=&hsa_ad=&hsa_src=x&hsa_tgt=&hsa_kw=&hsa_mt=&hsa_net=adwords&hsa_ver=3&gclid=CjwKCAiArOqOBhBmEiwAsgeLmSNKfEBC1-28BCMo191qR1ob9hYz25-6s2YIRHY6avjUPN5QJen5lBoCdBsQAvD_BwE
https://log-it.tech/2020/05/28/prometheus-metrics-to-pandas-data-frame/
https://ricardorocha.io/blog/prometheus-metrics-in-pandas/
http://www.fhdq.net/sx/12.html
https://github.com/AICoE/prometheus-data-science#ad-hist-summ
https://www.math.pku.edu.cn/teachers/lidf/course/fts/ftsnotes/html/_ftsnotes/fts-arma.html
https://www.statsmodels.org/stable/index.html
https://zhangxin.liumengyang.xyz/shi-jian-xu-lie-mo-xing-xue-xi/
https://blog.csdn.net/qq_33333002/article/details/105998201
https://mp.weixin.qq.com/s/dTEpaADo9gM8V62176GnuA
https://towardsdatascience.com/machine-learning-part-19-time-series-and-autoregressive-integrated-moving-average-model-arima-c1005347b0d7