Data Mining, Time Series Analysis, Data Mining Tasks, Data Mining Models, Measuring Components, Times Series Graphs

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MCA 3rd SEMESTER ( Lateral Entry )
DATA MINING
TIME SERIES ANALYSIS
SUBMITTED TO : -
Dr. S. Senthil
SUBMITTED BY : -
Tanishq Soni

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Introduction
Data Mining – Definition
 It is the process of identifying knowledge hidden from large volumes of data.
 It is a process of extracting valid, useful and ultimately understandable patterns in data.
 It is a technology that blends traditional data analysis methods with sophisticated algorithms for
processing large volumes of data.

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Data Mining
Data Knowledge
• Artificial Intelligence
• Computer Science
• Statistics
• Information Retrieval

4. 4DataMiningTasks
 Descriptive
What happened?
 Diagnostic
Why did it happen?
 Predictive
When will it happen?
 Prescriptive
How can we make it happen?

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DataMiningModelsandTasks

6. 6DataMiningTasks
 Predictive Tasks
 Objective of these tasks is to predict the value of a particular attribute based on the values of other attributes.
 Attribute to be predicted – Target or Dependent variable.
 Attributes used for making the prediction – Explanatory or Independent variable.
 Makes prediction about values of data using known results found from different data.
 May be made based on the use of other historical data.

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Time Series Analysis
Value of an attribute is examines as it varies over time.
A time series plot is used to visualize the time series.
E.g. Stock Market.
Three basic functions performed in time series analysis:
Distance measures– used to measure the similarity between the
data.
Structure of the line – is examined to predict its behaviour.
Historical time series plot can be used to predict future values.

8. Stocks data Sales Goods Consumption
Images
motion capture
Handwritten Character Recognition
DNA sequences
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9. 4MeasuringComponents:-
 Long-term or trend movement,
 Seasonal Movements or Seasonal Variation,
 Irregular or Random Movements and
 Periodicity analysis
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10. (1.)Long-term or trend movement:
 These indicate the general direction in which a time series graph is moving over
a long interval of time. This movement is displayed by a trend curve or trend
line.
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11. (2.) Seasonal Movements or
Seasonal Variation:
 These movements are due to events that reoccur annually such as the sudden
increase in data. Hence seasonal movements are identical if the pattern follows the
match of successive years.
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12. (3.) Irregular or Random Movements:
 These characterize the sporadic motion of time-series due to random or chance events
such as labour disputes, floods or announced personal changes within the companies.
Sequential pattern mining is the mining of frequently pattern related to time or other
frequencies.
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(4.) Periodicity analysis:
Periodicity analysis is the mining of periodical pattern that is the search for recurring
pattern in time-series database.
It can be applied to area such as season, tides planet trajectories, daily power consumption,
daily traffic pattern.

14. Graphs can be drawn to illustrate a set of time series data. Time is always plotted on an even scale along the
horizontal axis. The variable being measured is plotted on the vertical axis.
Example
The table shows the sales of a company in millions of dollars.
Showing this on a time series graph:
The features of this graph are its cyclical nature and an apparent upward long term trend.
Drawing Time Series Graphs
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15. Year 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000
Sales
$m
12 3 9 24 33 48 27 15 36 57 51 24 45 63 57
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