Journal of Biostatistics and Epidemiology 2015. 1(3-4):86-92.

Comparison of auto regressive integrated moving average and artificial neural networks forecasting in mortality of breast cancer
Mohammad Moqaddasi-Amiri, Abbas Bahrampour


Background & Aim: One of the common used models in time series is auto regressive integrated moving  average  (ARIMA)  model.  ARIMA  will  do  modeling  only  linearly.  Artificial  neural networks (ANN) are modern methods that be used for time series forecasting.  These models can identify non-linear relationships  among data. The breast cancer has the most mortality of cancers among women. The aim of this study was fitting the both ARIMA and ANNs models on the breast cancer mortality and comparing the accuracy of those in parameter estimating and forecasting.

Methods & Materials: We used the mortality of breast cancer data for comparing two models. The data are the number of deaths caused by breast cancer in 105 months in Kerman province. Each of ARIMA and ANNs models is fitted and chose the best one of each method separately, with some diagnostic criteria. Then, the performance of them is compared a minimum of mean squared error and mean absolute error.

Results: This comparison shows that the performance of ANNs models in parameter estimating and forecasting is better than ARIMA model.

Conclusion:  It  seems  that  the  breast  cancer  mortality  has  a  non-linear  pattern,  and  the  ANNs approach can be more useful and more accurate than ARIMA method.


time series; neural networks; breast cancerauto regressive integrated moving average;mortality

Full Text:



Zhang GP. Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing 2003; 50: 159-75.

Imhoff M, Bauer M, Gather U, Löhlein D.Time series analysis in intensive care medicine. Applied Cardiopulmonary Pathophysiology 1997; 6(263): 81.

International Agency for Research on Cancer. The globocan project [Online]. [cited 2012]; Available from: URL:

Alvaro-Meca A, Debon A, Gil PR, Gil de Miguel A. Breast cancer mortality in Spain: has it really declined for all age groups? Public Health 2012; 126(10): 891-5.

Bae JM, Jung K, Won YJ. Estimation of cancer deaths in Korea for the upcoming years. J Korean Med Sci 2002; 17(5): 611-5.

Yasmeen F, Hyndman RJ, Erbas B.Forecasting age-related changes in breast cancer mortality among white and black US women: a functional data approach. Cancer

Epidemiol 2010; 34(5): 542-9.

Frank RJ, Davey N, Hunt SP. Time Series Prediction and Neural Networks. Journal of Intelligent and Robotic Systems 2001; 31(1-3): 91-103.

Zhang G, Patuwo BE, Hu MY. Forecasting with artificial neural networks: The state of the art. International Journal of Forecasting 1998; 14(1): 35-62.

Kaastr I, Boyd M. Designing a neural network for forecasting financial and economic time series. Neurocomputing 1996; 10(3): 215-36.

Hyndman RJ. Forecast package for R [Online]. [cited 2014 May 8]; Available from: URL:

Limas MC, Ordieres Mere JB, Marcos AG,Martinez FJ, de Pison Ascacibar F, Espinoza AV, et al. AMORE: A MORE flexible neural network package [Online]. [cited 2014 Apr 14]; Available from: URL:http://cran.r-

Lachtermacher G, Fuller JD. Back propagation in time-series forecasting.Journal of Forecasting 1995; 14(4): 381-93.

Kohzadi N, Boyd MS, Kermanshahi B, Kaastra I. A comparison of artificial neural network and time series models for forecasting commodity prices. Neurocomputing 1996; 10(2): 169-81.

Caire P, de France E, Hatabian G, Muller C.Progress in forecasting by neural networks. Baltimore, MD: Neural Networks, IJCNN, International Joint Conference on; 1992.

Kang SY. An investigation of the use of feedforward neural networks for forecasting [Thesis]. Kent, OH: Kent State University 1992.

Sharda R, Patil RB. Connectionist approach to time series prediction: an empirical test. Journal of Intelligent Manufacturing 1992;3(5): 317-23.

Portugal MS, de Pós-Graduação C. Neural networks versus time series methods: a forecasting exercise. Revista Brasileira de Economia 1995; 49(4): 611-29.

Chakraborty K, Mehrotra K, Mohan C, Ranka S. Forecasting the behavior of multivariate time series using neural networks. Neural Networks 1992; 5(6): 961-70.


  • There are currently no refbacks.

Creative Commons Attribution-NonCommercial 3.0

This work is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported License which allows users to read, copy, distribute and make derivative works for non-commercial purposes from the material, as long as the author of the original work is cited properly.