Improved Neural Network Tool: Application to Societal Forecasting Problems

  • Amol C. Adamuthe
  • Ramkrishna V. Vhatkar
Conference paper


Time series forecasting is an important issue for many individuals and enterprises for decision making and planning. Researchers are investigated many time series forecasting problems related to demand, sales, technology, stock, financial forecasting etc. Recent research trend suggests that artificial neural networks are one of the important alternatives to traditional statistical methods. This paper presents improved ANN tools for solving social forecasting problems. This paper presents two multivariate time series forecasting problem for financial organization and agricultural sector. Different models of ANN are developed with varying hidden nodes. Learning rate of the proposed model is adaptive based on the results of past iterations. The experimental result shows that for validation and training, ANN with extended adaptive learning rate outperforms other two variations. Results show that the performance of all ANN increases with a number of hidden nodes up to a certain limit.


Artificial neural network (ANN) Adaptive learning rate Forecasting 


  1. 1.
    Montgomery DC, Jennings CL, Kulahci M (2015) Introduction to time series analysis and forecasting. Wiley, New YorkzbMATHGoogle Scholar
  2. 2.
    De Gooijer JG, Hyndman RJ (2006) 25 years of time series forecasting. Int J Forecast 22(3):443–473CrossRefGoogle Scholar
  3. 3.
    Palit AK, Popovic D (2006) Computational intelligence in time series forecasting: theory and engineering applications. Springer Science & Business Media, DordrechtzbMATHGoogle Scholar
  4. 4.
    Lahiri SK, Ghanta KC (2009) Artificial neural network model with the parameter tuning assisted by a differential evolution technique: the study of the holdup of the slurry flow in a pipeline. Chem Ind Chem Eng Q 15(2):103–117CrossRefGoogle Scholar
  5. 5.
    Rahul GK, Khurana M (2010) A: comparative study review of soft computing approach in weather forecasting. WorkGoogle Scholar
  6. 6.
    Panchal FS, Panchal M (2014) Review on methods of selecting number of hidden nodes in artificial neural network. Int J Comput Sci Mob Comput 3(11):455–464MathSciNetGoogle Scholar
  7. 7.
    Choi TM, Hui CL, Ng SF, Yu Y (2012) Color trend forecasting of fashionable products with very few historical data. IEEE Trans Syst Man Cybern Part C Appl Rev 42(6):1003–1010CrossRefGoogle Scholar
  8. 8.
    Feng J, Liu T, Zeng L, Wang D, Wang X (2017) Research and application of grey neural network in equipment life prediction. In: 3rd IEEE International Conference on Computer and Communications (ICCC), pp 1990–1994. IEEE (2017, December)Google Scholar
  9. 9.
    Mosbah H, El-Hawary M (2016) Hourly electricity price forecasting for the next month using multilayer neural network. Can J Electr Comput Eng 39(4):283–291CrossRefGoogle Scholar
  10. 10.
    Kwon YK, Moon BR (2007) A hybrid neurogenetic approach for stock forecasting. IEEE Trans Neural Netw 18(3):851–864CrossRefGoogle Scholar
  11. 11.
    Tiňo P, Schittenkopf C, Dorffner G (2001) Financial volatility trading using recurrent neural networks. IEEE Trans Neural Netw 12(4):865–874CrossRefGoogle Scholar
  12. 12.
    Hicham A, Mohamed B (2012) A model for sales forecasting based on fuzzy clustering and Back-propagation neural networks with adaptive learning rate. In: International conference on complex systems, pp. 1–5, IEEE (2012, November)Google Scholar
  13. 13.
    Magdum SK, Adamuthe AC (2017) Construction cost prediction using neural networks. ICTACT J Soft Comput 8(1):1549–1556CrossRefGoogle Scholar
  14. 14.
    Li J, Cheng JH, Shi JY, Huang F (2012) Brief introduction of back propagation (BP) neural network algorithm and its improvement. In: Advances in computer science and information engineering. Springer, Berlin/Heidelberg, pp 553–558CrossRefGoogle Scholar
  15. 15.
    Kumar P, Walia E (2006) Cash forecasting: an application of artificial neural networks in finance. IJCSA 3(1):61–77Google Scholar
  16. 16.
    Veenadhari S, Misra B, Singh C (2014) Machine learning approach for forecasting crop yield based on climatic parameters. In: International Conference on Computer Communication and Informatics (ICCCI), pp 1–5. IEEEGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Amol C. Adamuthe
    • 1
  • Ramkrishna V. Vhatkar
    • 1
  1. 1.Rajarambapu Institute of TechnologyRajaramnagarIndia

Personalised recommendations