$($WLO$1,$ WLO$2)$ Given the data

$x[n]=\{x[0]=1024,2131,2996,4045,x[4]=4999\}$

$($WLO$2,$WLO$3)[$Programming Question$]$ Use data in the column "close$/$last$"$ in "HistoricalQuotes.csv$".$ Assume that the average error is mean squared error, as

MSE$=\frac{1}{N}{\displaystyle \underset{i=1}{\overset{N}{}}}(hat(x)[n]-x[n]{)}^2$

where hat$(x)[n]$ is the predicted value and $x[n]$ is the true value. We will use the past data to predict the future data.

a$)$ Implement a $2^{nd}$ order adaptive FIR filter and calculate your average error.

b$)$ Implement a $2^{nd}$ order adaptive FIR filter with a bias and calculate your average error.

c$)$ Implement a $3=rd$ order adaptive FIR filter with calculate your average error.

d$)$ Implement a $3=rd$ order adaptive FIR filter with a bias and calculate your average error.

e$)$ Use logistic regression to predict the next value and compare it with the LMS based predictors.

f$)$ Which predictor is the best one?