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The main weakness of original Exponential Smoothing Method is that it can only provide point estimation. Hyndman (2002) proposed using state space framework to rewrite the original exponential smoothing algorithm and then give distribution assumption on the error terms to calculate the prediction interval. There are two types of error terms in the state space model: Additive and Multiplicative. The point estimator for these two models are the same but the prediction intervals are different.

This post is about Exponential Smoothing method, a prediction method for time series data. There are many forms of Exponential Smoothing method and the most basic ones are Single, Double and Triple (Holt-Winters) Exponential Smoothing. Some of the Exponential Smoothing forms can be written as ARIMA model; some of them can not and vice versa. Compared to ARIMA model, Exponential Smoothing method do not have strong model assumptions and it also can not add explanatory variables in the algorithm.

LDA and QDA are classification methods based on the concept of Bayes’ Theorem with assumption on conditional Multivariate Normal Distribution. And, because of this assumption, LDA and QDA can only be used when all explanotary variables are numeric. This post is my note about LDA and QDA, classification teachniques. All the contents in this post are based on my reading on many resources which are listed in the References part.