Update – i completed my analysis with linear regression and random forest model . I calculated the perdect tuning by changing window size to 10 and found out the best threshold for anomalies , my formula for threshold was x * std + mean , and chosing 0.5 as x gave us the best result and it makes as i realize because prediction errors during anomaly range arent that much bigger compare to errors in normal range . so thats why lowering down the x helped us . Also i created confusion matrix and get result for random forest and linear regression model, catching around 42 percent anomaly though only 12-13 % catched were actually correct . Tested the setup on multiple channels , some channels work perfectly and give some good results but some not and with no cleat pattern i end my case with lienar regression model and random forest here and moved towards implementing LSTM . Currently implemeting it and testing it and trying to improve the results . LSTM update will come in next devlog .