Artificial Neural Network Based Model for Forecasting of Inflation in India

被引:13
|
作者
Thakur, Gour Sundar Mitra [1 ]
Bhattacharyya, Rupak [2 ]
Mondal, Seema Sarkar [3 ]
机构
[1] Dr BC Roy Engn Coll, Dept Comp Sci & Engn, Durgapur, W Bengal, India
[2] Bijoy Krishna Girls Coll, Dept Math, Howrah, W Bengal, India
[3] Natl Inst Technol, Dept Math, Durgapur, W Bengal, India
关键词
Inflation forecasting; Artificial neural network; Back propagation algorithm;
D O I
10.1016/j.fiae.2016.03.005
中图分类号
O29 [应用数学];
学科分类号
070104 ;
摘要
Inflation can be attributed to both microeconomic and macroeconomic factors which influence the stability of the economy of any nation. With the raising of recession at the end of the year 2008, world communities started paying much contemplation on inflation and put enormous hard work to predict it accurately. Prediction of inflation is not a simple task. Moreover, the behavior of inflation is so complex and uncertain that both economists and statisticians have been striving to model and forecast inflation in an accurate way. As a result, many researchers have proposed inflation forecasting models based on different methods; however the accuracy is always being a major constraint. In this paper, we have analyzed the historical monthly economic data of India between January 2000 and December 2012 and constructed an inflation forecasting model based on feed forward back propagation neural network. Initially some critical factors that can considerably influence the inflation of India have been identified, then an efficient artificial neural network (ANN) model has been proposed to forecast the inflation. Accuracy of the model is proved to be satisfactory when compared with the forecasting of some well-known agencies.
引用
收藏
页码:87 / 100
页数:14
相关论文
共 50 条
  • [1] A Rainfall Forecasting Model Based on Artificial Neural Network
    Nong, Jifu
    Huang, Wenning
    [J]. 2012 2ND INTERNATIONAL CONFERENCE ON APPLIED ROBOTICS FOR THE POWER INDUSTRY (CARPI), 2012, : 1249 - 1252
  • [2] Forecasting model for the incidence of hepatitis A based on artificial neural network
    Guan, Peng
    Huang, De-Sheng
    Zhou, Bao-Sen
    [J]. WORLD JOURNAL OF GASTROENTEROLOGY, 2004, 10 (24) : 3579 - 3582
  • [3] Study on the Model of Demand Forecasting Based on Artificial Neural Network
    Zhu Ying
    Xiao Hanbin
    [J]. PROCEEDINGS OF THE NINTH INTERNATIONAL SYMPOSIUM ON DISTRIBUTED COMPUTING AND APPLICATIONS TO BUSINESS, ENGINEERING AND SCIENCE (DCABES 2010), 2010, : 382 - 386
  • [4] Stock Market Forecasting Based on Artificial Neural Network Model
    Zhou Shaofu
    Xu Yang
    [J]. RECENT ADVANCE IN STATISTICS APPLICATION AND RELATED AREAS, PTS 1 AND 2, 2008, : 1119 - 1123
  • [5] Forecasting model for the incidence of hepatitis A based on artificial neural network
    Peng Guan Bao-Sen Zhou Department of Epidemiology
    [J]. World Journal of Gastroenterology, 2004, (24) : 3579 - 3582
  • [6] Forecasting inflation under globalization with artificial neural network-based thin and thick models
    Hu, Tsui-Fang
    Luja, Iker Gondra
    Su, H. C.
    Chang, Chin-Chih
    [J]. WCECS 2007: WORLD CONGRESS ON ENGINEERING AND COMPUTER SCIENCE, 2007, : 909 - +
  • [7] Combining forecasting model based on Grey theory and artificial neural network
    Zheng Deling
    Liu Cong
    Fang Wei
    Fang Tong
    [J]. Proceedings of the 24th Chinese Control Conference, Vols 1 and 2, 2005, : 1069 - 1072
  • [8] Forecasting volatility with a stacked model based on a hybridized Artificial Neural Network
    Ramos-Perez, Eduardo
    Alonso-Gonzalez, Pablo J.
    Javier Nunez-Velazquez, Jose
    [J]. EXPERT SYSTEMS WITH APPLICATIONS, 2019, 129 : 1 - 9
  • [9] Based on Artificial Neural Network Model of Regional Logistics Demand Forecasting
    Yang, Shu-xia
    Cui, Dan
    [J]. 3RD INTERNATIONAL CONFERENCE ON EDUCATION REFORM AND MODERN MANAGEMENT, 2016, 2016, : 151 - 155
  • [10] Oil demand forecasting for India using artificial neural network
    Jebaraj, S.
    Iniyan, S.
    [J]. INTERNATIONAL JOURNAL OF GLOBAL ENERGY ISSUES, 2015, 38 (4-6) : 322 - 341