Principal Component Analysis (PCA) is a powerful statistical technique for variable reduction, It used when variables are highly correlated. ... PCA incorporated with AI techniques to improve performance of many applications like image processing, pattern recognition, classification and anomaly detection.
Exponential smoothings methods are appropriate for non-stationary data (ie data with a trend and seasonal data). ARIMA models should be used on stationary data only. One should therefore remove the trend of the data (via deflating or logging), and then look at the differenced series.
rolling() function is a very useful function. It Provides rolling window calculations over the underlying data in the given Series object. Syntax: Series.rolling(window, min_periods=None, center=False, win_type=None, on=None, axis=0, closed=None)
ARIMA is an acronym for autoregressive integrated moving average. Its a model used in statistics and econometrics to measure events that happen over a period of time. The model is used to understand past data or predict future data in a series.