Fuzzy Logic

Fuzzy
Fuzzy System has been existed since 1920, that proposed by Lukasiewicz. Fuzzy System existed to handle gray interpretation, which means to handle variable that have non-crisp values or bold values (crisp values : True/False). Fuzzy System likely more handling the variable that have non-crisp value or we called degrees of membership, for example the variable that has values between 0..1

"FUZZY LOGIC IS A BRANCH OF THE MEMBERSHIP DEGREES COMPARED USING EXPRESS MEMBERSHIP (TRUE / FALSE)"

Fuzzy Logic
Fuzzy Logic more focused on quantitying and reasoning of fuzzy that comes from natural language. Value that comes from natural language is more known as Linguistic Variable / Fuzzy Variable . For example above

Fuzzy

Fuzzy Rule
On Fuzzy System, Linguistic Variable is used on Fuzzy Rule. Example of Fuzzy Rule :

Fuzzy Rule

Fuzzy Membership & Fuzzy Sets
In crisp logic, we just have 2 values, True and False, but in Fuzzy, we have membership degrees, or range 0 to 1 in values.
To make Fuzzy Sets, we need the membership function. To define membership function, there are many methods, for example : polling, define itself, questioner, etc. Example of fuzzy : Short (4..6) Medium (4,5..6,5) Tall (5..7) and the fuzzy sets are like this :

Fuzzy Sets

Fuzzy Time Series

Fuzzy Time Series is one of method that usually used for predicting. Fuzzy Time Series is combining of data time series and using fuzzy logic to create prediction for what's next. In Fuzzy Time Series the data that used is data time series, which is have many time sequences of data, and the variable can be only one. For example : Sales Total of Dept. Store X in 2000 - 2010. It's contain only 1 variable data which is Total Sales, but it has many sequence data, for example : per month, year.

To use this method, simply find the data that have sequence time,or we called data time series, then preprocessing the data, then using this Fuzzy Time Series to predict. We can preprocess data with many ways, for example : using Min-Max Normalization and Z-Score Normalization. This preprocessing is done for making the data better. In Fuzzy Time Series, it will be good, if the differences (lag) between data is not too high, because it will take effect on the prediction.

Well after the data being processed, simply use this method. There are many steps of Fuzzy Time Series. First step, Making the Interval.
The interval is determined by look at the data. We should find the data maximal, minimal, lag between data, and average of lag.After we find these attributes, we can calculate data by Range/(Avg.lag/2). Range is Dmax - Dmin.