Forecasting Food Inflation in Nigeria using ARIMA and ELM Techniques
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Aikake, H., "A new look at the statistical model identification", IEEE Transactions on Automatic Control, 1974, vol. 19(6). pp. 716–723
Anil Kumar Mahto, M. Afshar Alam, Ranjit Biswas, Jawed Ahmed, and ShahImran Alam. Short-Term Forecasting of Agriculture Commodities in Context of Indian Market for Sustainable Agriculture by Using the Artificial Neural Networks. Hindawi Journal of Food Quality Volume 2021, Article ID 9939906, 13 pages
Annema, A.; Hoen, K.; Wallinga, H. Precision requirements for single-layer feedforward neural networkss. In Proceedings of the Fourth International Conference on Microelectronics for Neural Networkss and Fuzzy Systems, Turin, Italy, 26–28 September 1994; pp. 145–151
Box, G. E. P., and Jenkins, G. M., Time Series Analysis: Forecasting and Control, revised edition, San Francisco: Holden Day, 1976
Cheng Lian, Zhigang Zeng, Wei Yao and Huiming Tang., Ensemble of extreme learning machine for landslide displacement prediction based on time series analysis, Neural Comput & Applic, 2014, 24:99–107
Dickey, D. A., and Fuller, W. A., „Distribution of the Estimators for Autoregressive Time Series with a Unit Root‟, Journal of the American Statistical Association, (1979), 74,(Pg 427–31).
Emi Nakamura., Inflation forecasting using a neural networks. Economics letter 86. 2005, 373-378
G. B. Huang, Learning capability and storage capacity of two-hidden-layer feedforward networks. IEEE Trans Neural Netw ((2003), 14(2):274–281
Gideon G. Goshit, Paul Terhemba Iorember, short-term inflation rate forecasting in Nigeria: an autoregressive integrated moving average (ARIMA) and artificial neural networks (ANN) models. Journal of economic and financial issues, (2018), volume 4, number
Huang, G.-B.; Zhu, Q.-Y.; Siew, C.-K. Extreme learning machine: A new learning scheme of feedforward neural networkss. In Proceedings of the 2004 IEEE International Joint Conference on Neural Networkss, Budapest, Hungary, 25–29 July 2004; pp. 985–990
Huang, S.; Li, C. Distributed extreme learning machine for nonlinear learning over networks. Entropy 2015, 17, 818–840.
Liang, N.-Y.; Huang, G.-B.; Saratchandran, P.; Sundararajan, N. A fast and accurate online sequential learning algorithm for feedforward networkss. IEEE Trans. Neural Netw. 2006, 17, 1411–1423.
Olatunji G.B., Omotesho O. A., Ayinde O. E. and Ayinde K., Determinants of inflation in Nigeria: A co-integration approach, Contributed paper presented at the Joint 3rd African Association of Agriculture Eonomists (AAAE) and 48th Agricultural Economists Association of South Africa (AEASA) Conference, Cape Town, South Africa, September 19 – 23, 2010.
Phillips, P. C. B., Perron, P., Testing for a unit root in a time series regression. Biometrika, (1988) Vol. 75, No. 2, pp. 335–346.
Rampal Singh, and S. Balasundaram.., Application of Extreme Learning Machine Method for Time Series Analysis. International Journal of Electrical and Computer Engineering (2007) 2:8
Schwarz, G. E., "Estimating the dimension of a model." Annals of Statistics (1978), 6(2): 461– 464.
Singh,R.;Balasundaram,S.Applicationofextremelearningmachinemethodfortimeseriesanalysis. Int. J. Intell. Technol. 2007, 2, 256–262.
DOI: http://dx.doi.org/10.52155/ijpsat.v29.2.3751
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