Alternative preprocessing techniques

For the sake of space and your time, this chapter introduced and applied three filtering and smoothing classes of algorithms. Moving averages, Fourier series, and Kalman filter are far from being the only techniques used in cleaning raw data. The alternative techniques can be classified into the following categories:

• Autoregressive models that encompass Auto-Regressive Moving Average (ARMA), Auto-Regressive Integrated Moving Average (ARIMA), generalized autoregressive conditional heteroscedasticity (GARCH), and Box-Jenkins rely on some form of autocorrelation function
• Curve-fitting algorithms that include the polynomial and geometric fit with ordinary least squares method, non-linear least squares using the Levenberg-Marquardt optimizer and probability distribution fitting
• Non-linear dynamic systems with Gaussian noise such as particle filter
• Hidden Markov models as described in Hidden Markov models section of Chapter 7, Sequential data models