Time series models are essential tools for analyzing and forecasting data that is indexed in time order. This tutorial will guide you through the basics of time series analysis and introduce some common models used in practice.
Common Time Series Models
- ARIMA (AutoRegressive Integrated Moving Average): A popular model that captures the autocorrelation in time series data.
- SARIMA (Seasonal ARIMA): An extension of ARIMA that incorporates seasonal effects.
- Prophet: A popular open-source forecasting tool developed by Facebook.
ARIMA Model
The ARIMA model is a combination of three processes: autoregression (AR), differencing (I), and moving average (MA).
- AR: The current value of the series is a linear combination of its past values.
- I: The series is differenced to remove any non-stationary components.
- MA: The current value of the series is a linear combination of the error terms from the moving average process.
Prophet Model
Prophet is a powerful tool for forecasting time series data. It is particularly useful for data with strong seasonal patterns and holiday effects.
- Seasonality: Prophet automatically detects and handles seasonality in the data.
- Holiday Effects: Prophet allows you to specify holiday effects that can impact the forecast.
Learning Resources
For further reading, we recommend the following resources:
Time Series Data