Comparative Study of ARIMA, SARIMA and Hybrid   (ARIMA + ANN and SARIMA + ANN) Models for   Wholesale Monthly Average Price of Tomato and Onion

Sanjeev; Pushpa; Vikram; Preeti; Pawan Kumar

doi:10.56093/jisas.v77i03.171456

Authors

Sanjeev Chaudhary Charan Singh Haryana Agricultural University, Hisar
Pushpa Chaudhary Charan Singh Haryana Agricultural University, Hisar
Vikram Chaudhary Charan Singh Haryana Agricultural University, Hisar
Preeti Chaudhary Charan Singh Haryana Agricultural University, Hisar
Pawan Kumar Maharana Pratap Horticultural University, Karnal

https://doi.org/10.56093/jisas.v77i03.171456

Keywords:

ARIMA; SARIMA; Hybrid; Time series price forecasting; Linear and non-linear patterns

Abstract

Time series price forecasting is an important area of forecasting in which past observations of the same variable are collected and analysed to develop a model describing the underlying relationship. In this paper, to compare the forecasting performance of ARIMA (Autoregressive Integrated Moving Average), SARIMA (Seasonal Autoregressive Integrated Moving Average) hybrid (ARIMA + ANN (Artificial Neuron Network) and SARIMA + ANN) techniques for all India wholesale monthly average price time series of tomato and onion crop. The ARIMA and SARIMA techniques are used to capture the linear pattern of data. The ANN technique is used to capture the nonlinear patterns of the residuals obtain from ARIMA and SARIMA techniques. Empirical results indicate that hybrid (SARIMA + ANN) technique is effective way to improve the forecasting performance for price series of tomato and onion crop on the basis of least value of error measure such as RD (%), RMSE, MAPE and MAE.

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References

Adebiyi, A.A., Adewumi, A.O. and Ayo, C.K. 2014. Comparison of ARIMA and Artificial Neural Networks Models for Stock Price Prediction. Journal of Applied Mathematics, 7.

Ayub, S. and Jafri, Y.Z. 2020. Comparative Study of an ANN-ARIMA hybrid model for predicting Karachi stock price. American Journal of Mathematics and Statistics, 10, 1-9. Doi: 10.5923/j.ajms.20201001.01.

Box, G.E.P. and Jenkins, G.M. 1970. Time series analysis: forecasting and control. San Francisco: Holden-Day.

Brock, W.A., Scheinkman, J.A., Dechert, W.D. and LeBaron, B. 1996. A Test for Independence Based on the Correlation Dimension. Econometric Reviews, 15, 197-235.

Chandran, K.P. and Pandey, N.K. 2007. Potato price forecasting using seasonal ARIMA approach. Potato Journal, 34(1), 1-2.

Chen, K.Y. and Wang, C.H. 2007. A Hybrid SARIMA and support vector machines in forecasting the production values of the machinery industry in Taiwan. Expert Systems with Applications, 32, 254-264.

Choudhary, K., Jha, G.K., Das, P. and Chaturvedi, K.K. 2019. Forecasting potato price using ensemble artificial neural networks. Indian Journal of Extension Education, 55, 73-77.

Friedman, M. 1937. The Use of Ranks to Avoid the Assumption of Normality Implicit in the Analysis of Variance. Journal of the American Statistical Association, 32(200), 675-701.

Jha, Girish K. and Sinha, K. 2012. Time delay neural networks for time series prediction: an application to the monthly wholesale price of oilseeds in India. Neural Computing and Applications. Springer.

Kambo, R., Pillai, J. and Soni, S. 2018. Agriculture commodity price forecasting using soft computing techniques: a survey. International Journal of Advanced in Management, Technology and Engineering Sciences, 8, 1580-1583.

Khashei, M. and Hajirahimi, Z. 2018. A Comparative Study of Series ARIMA/MLP Hybrid Models for Stock Price Forecasting. Communications in Statistics - Simulation and Computation, pp. 1-16. https://doi.org/10.1080/03610918.2018.1458138

.

Kruskal, W.H. and Wallis, W.A. 1952. Use of Ranks in One-Criterion Variance Analysis. Journal of the American Statistical Association, 47(260), 583–621.

Liang, Y.H. 2009. Combining seasonal time series ARIMA method and neural networks with genetic algorithms for predicting the production value of the mechanical industry in Taiwan. Neural Computing and Applications, 18, 833-841.

Rahman, N.M.F. 2010. Forecasting of boro rice production in Bangladesh: an ARIMA Approach. Journal of Bangladesh Agricultural University, 8(1), 103–112.

Sivamani, S., Venkatesan, S.K., Lee, M., Park, J., Cho, Y. and Shin, C. 2019. A study of seasonal ARIMA model-based forecasting method for intelligent food control in a livestock environment. International Journal of Engineering & Technology, 8, 555-564.

Temizel, T.T. and Casey, M.C. 2005. A comparative study of autoregressive neural network hybrids. Neural Networks, 18, 781-789.

Tseng, F., Yu, H.C. and Tzeng, G.H. 2002. Combining neural network model with seasonal time series ARIMA model. Technological Forecasting & Social Change, 66, 71-87.