## Exponential Smoothing

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.

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## LDA and QDA

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.

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## Naive Bayes Classifier

Naive Bayes Classifier is a simple and intuitive method for the classification. The algorithm is based on Bayes’ Theorem with two assumptions on predictors: conditionally independent and equal importance. This technique mainly works on categorical response and explanatory variables. But it still can work on numeric explanatory variables as long as it can be transformed to categorical variables. This post is my note about Naive Bayes Classifier, a classification teachniques.

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