TY - CHAP
T1 - Nowcasting of selected imports and exports of Bangladesh
T2 - Comparison among traditional time series model and machine learning models
AU - Hossain, Md Moyazzem
AU - Abdulla, Faruq
AU - Rahman, Azizur
N1 - Publisher Copyright:
© 2023 selection and editorial matter, Priyanka Harjule, Azizur Rahman, Basant Agarwal and Vinita Tiwari, individual chapters, the contributors.
PY - 2023
Y1 - 2023
N2 - Nowcasting, a combination of “now” and “forecast”, is the estimation of a target variable's current state, or a close approximation of it, either forwards or backwards in time, utilizing information that is available in a more timely manner. It has a wide range of applications, all of which attempt to supplement and assist users' decisions. This research applied auto-regressive moving average, neural network models, and support vector regression technique for modelling and nowcasting selected imports and exports in Bangladesh considering annual data from 1976–2020. The findings revealed that support vector models had superior performance compared to the other models considered in this study. In economic growth modelling and nowcasting purpose, the author recommends using the machine learning methodologies. It also suggests that the results be compared to classic econometric and time series models considering other variables with longer data periods.
AB - Nowcasting, a combination of “now” and “forecast”, is the estimation of a target variable's current state, or a close approximation of it, either forwards or backwards in time, utilizing information that is available in a more timely manner. It has a wide range of applications, all of which attempt to supplement and assist users' decisions. This research applied auto-regressive moving average, neural network models, and support vector regression technique for modelling and nowcasting selected imports and exports in Bangladesh considering annual data from 1976–2020. The findings revealed that support vector models had superior performance compared to the other models considered in this study. In economic growth modelling and nowcasting purpose, the author recommends using the machine learning methodologies. It also suggests that the results be compared to classic econometric and time series models considering other variables with longer data periods.
KW - forecasting
KW - auto-regressive moving average
KW - regression technique
KW - support vector models
KW - time series models
UR - http://www.scopus.com/inward/record.url?scp=85162678407&partnerID=8YFLogxK
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UR - https://www.routledge.com/Computational-Statistical-Methodologies-and-Modeling-for-Artificial-In/Agarwal-Harjule-Rahman-Tiwari/p/book/9781032170800
U2 - 10.1201/9781003253051-8
DO - 10.1201/9781003253051-8
M3 - Chapter (peer-reviewed)
AN - SCOPUS:85162678407
SN - 9781032170800
SP - 159
EP - 177
BT - Computational statistical methodologies and modelling for Artificial Intelligence
A2 - Harjule, Priyanka
A2 - Rahman, Azizur
A2 - Agarwal, Basant
A2 - Tiwari, Vinita
PB - CRC Press
CY - Boca Raton
ER -