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.