IMPLEMENTATION OF DEEP LEARNING METHOD FOR INFLATION PROJECTIONS IN SOUTH SUMATRA PROVINCE
Low and stable inflation is a prerequisite for sustainable economic growth. In formulating and implementing monetary policy to maintain macroeconomic stability, inflation forecasts play a key role considering the lag of effect of monetary policy on inflation. Traditional statistical methods such...
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| Format: | Theses |
| Language: | Indonesia |
| Online Access: | https://digilib.itb.ac.id/gdl/view/63862 |
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| Institution: | Institut Teknologi Bandung |
| Language: | Indonesia |
| Summary: | Low and stable inflation is a prerequisite for sustainable economic growth. In
formulating and implementing monetary policy to maintain macroeconomic
stability, inflation forecasts play a key role considering the lag of effect
of monetary policy on inflation. Traditional statistical methods such as
Autoregressive Integrated Moving Average (ARIMA) are popular methods
used in forecasting and analyzing time-series data. However, the existence
of limitations such as the assumption of models that are stationary and linear
becomes an obstacle to this method to be able to predict economic problems
that are generally nonlinear. Artificial neural network (ANN) models were
introduced as a new approach to forecasting. In this study, a neural network
model will be built to predict the inflation rate of South Sumatra Province.
The result shows that the ANN model with a single layer was able to provide
better South Sumatra’s inflation prediction results compared to the ARIMA
model. |
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