偏度 的計算公式:
- 如果 k3> 0,分佈為正偏(右偏),資料分佈中有較多的數據點集中在低值區域,而右尾較長,代表有少數較大的數值偏離平均數使得 (X-u)的3次方後正更多.
- 如果 k3 < 0,分佈為負偏(左偏),資料分佈中有較多的數據點集中在高值區域,而左尾較長,代表有少數較小的數值偏離平均數使得 (X-u)的3次方後負更多.
偏度 的計算公式:
- 趨勢線: 用原始資料的移動平均值(MA)作為長期走勢的觀察. (移動窗口可以自己設定)
- 週期性 (Seaonality): 將原始資料減去趨勢線可以強調週期性的特徵
- 雜訊(Residual) : 原始資料減去趨勢線再減去週期性線, 剩下的值認定是噪訊(隨機波動)或短期的變化。
ACF圖( X軸為不同的lag k, Y軸為ACF值)
底下ACF圖說明序列資料沒有存在週期性.
ACF圖( X軸為不同的lag k, Y軸為ACF值)
底下ACF圖說明序列資料存在一個週期性, 在Lag n*K 處都有差不多的峰值, 表示其週期性為 k
A botnet is a network of compromised computers or devices, often referred to as "bots" or "zombies," that are controlled remotely by a malicious actor (known as a "botmaster"). These devices are typically infected with malware, allowing the botmaster to execute various commands on them without the device owner’s knowledge.
Here are some common uses and dangers of botnets:
Distributed Denial of Service (DDoS) Attacks: Botnets are often used to flood a target server or website with traffic, overwhelming it and causing it to crash or become unavailable to users.
Spam Distribution: They can be used to send out massive amounts of spam emails or phishing messages, which can lead to further infections or fraud.
Credential Stuffing: Botnets may attempt to use stolen usernames and passwords on different sites, automating the process to try many combinations quickly.
如何同時衡量模型的正確性與長期穩定性?
Home Credit - Credit Risk Model Stability
Create a model measured against feature stability over time
https://www.kaggle.com/competitions/home-credit-credit-risk-model-stability/overview
gini = 2 * AUC - 1 (AUC:0~1)
stability metric = mean(gini) + 88.0 * min(0, a) - 0.5 * std(residuals)
針對預測能力呈現下滑趨勢給予懲罰 :即 a < 0 的情況
穏定性評估: 計算 residuals 的標準差, 愈小表示模型愈穏定. 最好是 0
[References ]