Forecasting Imports in OECD Member Countries and Iran by Using Neural
  Network Algorithms of LSTM
Soheila Khajoui Saeid Dehyadegari Sayyed Abdolmajid Jalaee
Artificial Neural Networks (ANN) which are a branch of artificial
intelligence, have shown their high value in lots of applications and are used
as a suitable forecasting method. Therefore, this study aims at forecasting
imports in OECD member selected countries and Iran for 20 seasons from 2021 to
2025 by means of ANN. Data related to the imports of such countries collected
over 50 years from 1970 to 2019 from valid resources including World Bank, WTO,
IFM,the data turned into seasonal data to increase the number of collected data
for better performance and high accuracy of the network by using Diz formula
that there were totally 200 data related to imports. This study has used LSTM
to analyse data in Pycharm. 75% of data considered as training data and 25%
considered as test data and the results of the analysis were forecasted with
99% accuracy which revealed the validity and reliability of the output. Since
the imports is consumption function and since the consumption is influenced
during Covid-19 Pandemic, so it is time-consuming to correct and improve it to
be influential on the imports, thus the imports in the years after Covid-19
Pandemic has had a fluctuating trend.