Uncertainty can be avoided by a set of simplifying

assumptions about the phenomenon concerned suing fuzzy inference system. For

instance, most of the probability graph point of measure is classified on

reality streaming of classes using Fuzzy inference system, test case, match

results relevance score and fuzzy implications are measures with fuzzification

time logic methods. In many scientific and technological institutions

determinism dominates the education systems.

3.1 Time series evaluation

A time series is a set of observations of a variable of

interest, representing a sequence of observations ordered in time. The variable

is observed in discrete temporal points, usually equally spaced, and the

analysis of such temporal behavior involves a description of the process or of the

phenomenon that generated the sequence with time logic fuzzy rule match case

points. A time series can be defined by Equation 1,

Sum S=

{St1+St2+St3,….Stn}……………………..(1)

Sum L={Xt1+Xt2+….Xtn}…………………….(2)

Where t is the temporal index and N is

the number of observations. Therefore, Xt is a

sequence of observations ordered and equally spaced in time. The application of

forecasting techniques relies on the ability to identify underlying regular

patterns in the dataset that make it possible to create a model capable of

generating subsequent temporal patterns.

4. Proposed implementation of MLS

-PSO

The

system for matching time series will be re implemented to provide the support

for mining time series in the frame of interactive sessions. A relational

database system will be used to store the time series the configuration data

and the classifiers which will improve the maintainability of the system to set

with time logic fuzzy inference system and allow the re usability of the

classifiers computed in the previous sessions Furthermore the system will

provide for the closer integration of the matching algorithm with the

classification system which will improve the speed of the matching algorithm

Finally we are currently working on the comparison of the presented algorithm

with the cross correlation method for dating sequences.

The

main intensive resource carried out the rule on streaming of time series

process with relational probability measure with fixing time logic fuzzy rule

system. The fuzzy generate linguistic graph point based summaries to find he

most case closeness point of uncertain data from nonlinear dataset. Before

anise case of particle swarm the fuzzy ruled out the search case matching point

procedure from linearity summarization of data. The rules deals the time set

logic principles with evaluation database series Time logic Ts.