@article{
 title = {Comparing the Forecasting Performance of ARIMA and Neural Network Model by using the Remittances of Bangladesh},
 type = {article},
 year = {2017},
 keywords = {Bangladesh,Forecasting,Model Selection,Remittance},
 pages = {1-12},
 volume = {34},
 id = {bc268c2f-5cb1-3b64-9727-4d242b4e1fbc},
 created = {2020-11-12T22:12:07.326Z},
 file_attached = {true},
 profile_id = {3d6b17c2-7de8-3a82-bf4f-ddb0e3081e5f},
 last_modified = {2021-05-05T17:29:59.445Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {At present, remittances play a crucial role in the economy of Bangladesh. However, the trends in the share of remittance to macroeconomic variable point to the growing importance of remittance in the Bangladesh economy and testify to the popular view that remittances are gradually providing more and more important contribution to our GDP over time. This paper considers a secondary data set collected from Bangladesh Bank over the period 1987-88 to 2014-15. This paper attempts here to forecast the total remittance received by Bangladesh with the help of ARIMA and Neural Network (NN) model and compare the performance of those models by using well-known model selection criteria. The time series plot is given in Figure 1(a) illustrates that remittance flows to Bangladesh have grown rapidly over the last two decades. Moreover, the fluctuations between forecasted series and original series are less by NN compared to ARIMA. In addition, based on the model selection criteria it may be concluded that the neural network performs better than ARIMA to forecast the remittance of Bangladesh.},
 bibtype = {article},
 author = {Hossain, Md Moyazzem and Abdulla, Faruq and Majumder, Ajit Kumar},
 journal = {Jahangirnagar University Journal of Statistical Studies}
}

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