1. 2013 conference on new media technology
Seng hansun
A New Approach of moving average method in Time Series analysis
2013 conference on new media technology
Tangerang - indonesia

2. introduction
A time series is a set of regular time-ordered observations of a quantitative characteristic of an individual or collective phenomenon taken at successive periods.

3. JKSE composite index data
goal
Time series characteristics
Time series analysis
Moving Average (MA)
SMA
WMA
JKSE composite index data
WEMA
forecasting
EMA

4. SMA, WMA, & ema
An Exponential Moving Average (EMA) is a type of WMA which assigns a weighting factor to each value in the data series according to its age.
A Simple Moving Average (SMA) is a common average of the previous n data points in time series data.
A Weighted Moving Average (WMA) is an improvement form of SMA. It gives a greater weight to more recent data than the older ones.

5. weMA Calculate the base value, , using equation:
for a given time series data and periods.
Using the base value obtained, calculate the forecasting value using formula
where is the value at time period , is the base value for a time period , and represents the degree of weighting decrease as in equation
Back to step (1) until each data point in the time periods given ended.

6. System specification WEB BASED SYSTEM Windows 7 Professional 64-bit
Processor Intel ® Core ™ i5
Installed Memory MB
280 GB hard disk space
12.1” monitor, keyboard, optical mouse
PHP Excel Reader library
JPGraph library
PHP version 5.3.1

7. Experimental
50 JKSE composite index data taken weekly from 13 August 2012 to 29 July 2013.

8. Experimental result
Simple Moving Average

9. Experimental result
Weighted Moving Average

10. Experimental result
Exponential Moving Average

11. Experimental result
Weighted Exponential Moving Average

12. conclusions
The weighted exponential moving average (WEMA) method can be used to forecast the JKSE composite index data.
Market traders can use the proposed method to take the best decision on buying or selling their shares based on the condition of the previous stock market data.

13. Future work
Taking a comprehensive study to analyze the advantages and disadvantages of the proposed method compare to the other moving average methods.

14. references
OECD: Glossary of Statistical Terms, last accessed August 2, 2013.
Subanar and Suhartono, Wavelet Neural Networks untuk Peramalan Data Time Series Finansial, Program Penelitian Ilmu Dasar Perguruan Tinggi, Yogyakarta: FMIPA UGM, 2009.
Boediono and W. Koster, Teori dan Aplikasi Statistika dan Probabilitas, Bandung: PT. Remaja Rosdakarya, 2001.
B. Render, R.M. Stair Jr., and M.E. Hanna, Quantitative Analysis for Management, 8th ed., New Jersey: Pearson Education, Inc., 2003.
M. Stevenson and J.E. Porter, “Fuzzy time series forecasting using percentage change as the universe of discourse,” World Academy of Science, Engineering and Technology, vol. 27, no. 55, 2009, pp ,
S. Hansun, “Peramalan data IHSG menggunakan fuzzy time series,” Indonesian Journal of Computing and Cybernetic Systems (IJCCS), vol. 6, no. 2, pp , July 2012.
S. Hansun and Subanar, 2011, “Penerapan pendekatan baru metode fuzzy-wavelet dalam analisis data runtun waktu,” Prosiding Seminar Nasional Ilmu Komputer (SEMINASIK) GAMA, Yogyakarta, Indonesia, pp
S. Hansun, “Penerapan Pendekatan Baru Metode Fuzzy-Wavelet dalam Analisis Data Runtun Waktu,” thesis, Program Studi S2 Ilmu Komputer, Yogyakarta: FMIPA UGM, 2011.

15. references
S. Hansun, “Jakarta stock exchange forecasting using backpropagation neural networks,” in press.
A. Popoola, S. Ahmad, and K. Ahmad, “A fuzzy-wavelet method for analyzing non-stationary time series,” Proc. of the 5th International Conference on Recent Advances in Soft Computing RASC2004, United Kingdom: Nottingham, 2004, pp
A.O. Popoola, “Fuzzy-Wavelet Method for Time Series Analysis,” dissertation, Department of Computing, School of Electronics and Physical Sciences, Surrey: University of Surrey, 2007.
C. Murphy, “Moving averages tutorial,” jmsc7008spring2012/files/2010/02/MovingAverages.pdf, last accessed October 13, 2013.
N. E. Hwa, “Different uses of moving average (MA),” last accessed October 13, 2013.
S. Dash, “A comparative study of moving averages: simple, weighted, and exponential,” last accessed October 13, 2013.
Yahoo! Finance, last accessed August 7, 2013.