Trend analysis-of-time-series-data-using-data-mining-techniques

1. Trend Analysis of Time
Series Data using Data
mining Techniques

2. 2
Md. Shahiduzzaman
Assistant Professor, Department Of CSE(BUBT)
GuidedBY
Nowreen Haque
ID-17181103043
Raihan Sikdar
ID-17181103133
Md Momin
ID-17181103046
Intake:37-2

3. Table of contents
3
◍ Introduction
◍ Objectives
◍ What is Data Mining?
◍ Data Mining Process
◍ Framework for Time series Analysis of Trend
◍ Methodology, Approach and Dataset
◍ Applications
◍ Conclusion

4. Introduction
4
◍ Time series is one of the popular data types that can be found in many
domains such as business, medical, meteorological fields, etc.
Identifying potential trends in time series is important because it
imparts knowledge about what has taken place in the past and what will
take place in time to come. Trend analysis in the time series is the
practice of collecting and attempting to spot patterns. Various data
mining techniques such as clustering, classification, regression, etc. can
be used to expose those trends.

5. ◍ Objectives
A time series is a data set that tracks a sample over time. In particular,
a time series allows one to see what factors influence certain variables
from period to period. Time series analysis can be useful to see how a
given asset, security, or economic variable changes over time.
5
Describe Model Predict

6. ◍ WhatIs Datamining?
◍ Data mining is the exploration and analysis of data in order to
uncover patterns or rules that are meaningful. It is classified as a
discipline within the field of data science. Data mining techniques are
to make machine learning (ML) models that enable artificial
intelligence (AI) applications. An example of data mining within
artificial intelligence includes things like search engine algorithms and
recommendation systems.
◍
6

7. Data Mining
process
Data Cleaning
Data
Integration
Data Selection
Data
Transformation
Data Mining
Knowledge
Representation
1
2
6
5
4
3

8. Methodology,
Approach and Dataset
8

9. Methodology
A merging algorithm to represent each cluster using a
representative series. Trends are detected in a series using
Modified Mann-Kendall test. Used non-parametric Modified
Mann-Kendall (MK) test at 95% significance level, which is the
popular trend test for meteorological time series data. To
identify the practical significance of trends, Sen’s median slope
estimator method is used.
Dataset: The data set used here is obtained from the Indian
Meteorological Department (IMD), Pune. Precipitation time
series data of 624 districts of India for 100 years from 1901 to
2000 is analyzed.

10. 10
Input
Time
Series
Pre-
procession
Similarity
Measure
Clustering
Normalized
Time Series
Distance
Matrix
Merging
Time
Series
Trend
Analysis
Clustered
Time Series Objects
Representative
Time Series
Framework for Time seriesAnalysisof Trend

11. Algorithm: Merging time series
NPUT: k = no. of clusters form by AGNES
Ni = no. of time series (T.S.) in cluster i.
Pi = set containing Ni T.S. of cluster i.
OUTPUT: Representative Time Series for each cluster
1) for i = 1 to k
2) dist = sim (Pi, Ni)
3) Z = linkage (dist)
4) k = Ni + 1
5) for j = 1 to Ni-1
6) r = Z[j][1]
7) s = Z[j][2]
8) Pi[k] = (Pi[r] + Pi[s]) / 2
9) Increment k.
10) Remove rth and sth T.S. from Pi.
11) end for
12) Q[i] = Pi[k-1]
13) end for

12. Applications
12
Outlier/anomaly
detection
Examining shocks/unexpected variation
Association
analysis
Predictive analytics

13. Conclusion
13
◍ Time series analysis is a must for every company to understand seasonality,
cyclicality, trend and randomness in the sales and other attributes
◍ Trend is a pattern in data that shows the movement of a series to relatively
higher or lower values over a long period of time. In other words, a trend is
observed when there is an increasing or decreasing slope in the time series.
Trend usually happens for some time and then disappears, it does not repeat.

15. Thanks!
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