Understand
Forecasting time series data is an important subject in economics, business, and finance.
- Traditionally, there are several techniques to effectively forecast the next lag of time series data such as univariate Autoregressive (AR), univariate Moving Average (MA), Simple Exponential Smoothing (SES), and more notably Autoregressive Integrated Moving Average (ARIMA) with its many variations.
- In particular, ARIMA model has demonstrated its outperformance in precision and accuracy of predicting the next lags of time series.
- With the recent advancement in computational power of computers and more importantly developing more advanced machine learning algorithms and approaches such as deep learning, new algorithms are developed to forecast time series data.