Proceedings of the Conference on Advances in Communication and Control Systems-2013

Forecasting of economic data sets and there trends using time series data modeling

Authors
Sunil Bhaskaran
Corresponding Author
Sunil Bhaskaran
Available Online April 2013.
Abstract
Towards the end of the 20th century, we have seen an improved interest among Statisticians and Computer engineers to explore and extract data from data sources, by considering time as standard of measurement. However, some of the techniques used remains the same as that used in conventional data mining. As in the case of traditional data base systems, in time series data systems too the methods employed in capturing, indexing, representing and storing the data remains as the key issue. In time series data mining the indexing problem become very critical under the noisy conditions. The indexing problem, however, both in noisy and non noise conditions have exploded the database size. In time series data analysis mathematical/statistical models, that provide descriptions for sample data, (like data collected on global warming, flood forecasting system etc) are used. The method is also used to provide a statistical arrangement for describing the nature of a continues stream of data that fluctuate in a random fashion with respect to the time. A time series can be further defined as a collection of random variables, indexed according to the order they have been extracted. Hence we can assume a time series as a sequence of random variables t1,t2,t3,t4 …., where the random variables t1,t2 etc are the observed values with respect to the time. It is already been proved that statistical methods such as moving average can be effectively used in smoothening data flow. Financial market, equity markets are essentially non-linear in nature. My approach in predicting financial time series is tested in simulation studies using non-linear models. It is shown to have a good success rate of correct forecasting.
Open Access
This is an open access article distributed under the CC BY-NC license.

Download article (PDF)

Proceedings
Proceedings of the Conference on Advances in Communication and Control Systems-2013
Part of series
Advances in Intelligent Systems Research
Publication Date
April 2013
ISBN
978-90-78677-66-6
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Sunil Bhaskaran
PY  - 2013/04
DA  - 2013/04
TI  - Forecasting of economic data sets and there trends using time series data modeling
BT  - Proceedings of the Conference on Advances in Communication and Control Systems-2013
PB  - Atlantis Press
UR  - https://www.atlantis-press.com/article/6298
ID  - Bhaskaran2013/04
ER  -