A Comparison of Standard Statistical, Machine Learning and Deep Learning Methods in Forecasting the Time Series

Abstract

Macroeconomic indicator forecasting is a difficult task and the
macroeconomy’s complex operations and dynamic nature make it even more
difficult. Machine Learning and Deep Learning methodologies have been
investigated as alternatives to traditional forecasting methods because of
recent developments in computing power and the emergence of data. How
the Machine Learning and Deep Learning paradigms apply to a variety of
Macro datasets have been examined in this research paper. Few Machine
Learning and Deep Learning algorithms have been trained and their forecasting
accuracy has been compared with that of traditional statistical method ARIMA.