This is an evolving repo with time series data analyses. The intention is to explore traditional time series methods (e.g. ARIMA models) as well as more modern deep learning approaches.
Currently, two modeling frameworks are included
- ARIMA stands for Auto-Regressive Integrated Moving Average. This time series modeling framework accommodates patterns and structures, such as trends and seasonality.
- Profit is a procedure for forecasting time series data based on an additive model where non-linear trends are fit with yearly, weekly, and daily seasonality, plus holiday effects. It works best with time series that have strong seasonal effects and several seasons of historical data. Prophet is robust to missing data and shifts in the trend, and typically handles outliers well.