Research Article
Analysis of Time Series Data Using Maximal Overlap Discrete Wavelet Transform Autoregressive Moving Average
@INPROCEEDINGS{10.4108/eai.2-8-2019.2290519, author={Sella Nofriska Sudrimo and Kusman Sadik and I Made Sumertajaya}, title={Analysis of Time Series Data Using Maximal Overlap Discrete Wavelet Transform Autoregressive Moving Average}, proceedings={Proceedings of the 1st International Conference on Statistics and Analytics, ICSA 2019, 2-3 August 2019, Bogor, Indonesia}, publisher={EAI}, proceedings_a={ICSA}, year={2020}, month={1}, keywords={autoregressive moving average discrete wavelet transform maximal overlap discrete wavelet transform}, doi={10.4108/eai.2-8-2019.2290519} }- Sella Nofriska Sudrimo
Kusman Sadik
I Made Sumertajaya
Year: 2020
Analysis of Time Series Data Using Maximal Overlap Discrete Wavelet Transform Autoregressive Moving Average
ICSA
EAI
DOI: 10.4108/eai.2-8-2019.2290519
Abstract
The price of broiler chickens has fluctuations pattern or certain wave patterns. This study aims to predict broiler chicken price data that have to fluctuate and non-stationary using MODWT-ARMA models andARIMA models and also see the ability of MODWT-ARMA in increasing accuracy in predicting data. In this study, the data is separated using wavelet transforms namely MODWT into two-part is wavelet and smooth signal, then each signal is modeled using the ARMA model and the final of the process is to recombine all signals. The results show that the MODWT-ARMA model has a smaller RMSE and normalized error than the ARIMA which is 1175.97 and 0.68 for the MODWT-ARMA model while 2365.85 and 2.77 for the ARIMA model. The conclusion in this study, MODWT-ARMA can handle broiler chicken price data in Bogor better than the ARIMA model and can improve the accuracy of prediction results.