Time Series data is experimental data that has been observed at different points in time (usually evenly spaced, like once a day). For example, the data of airline ticket sales per day is a time series.
Time Series have several key features such as trend, seasonality, and noise.
n an ARIMA model there are 3 parameters that are used to help model the major aspects of a times series: seasonality, trend, and noise. These parameters are labeled p,d,and q.
- p is the parameter associated with the auto-regressive aspect of the model
- d is the parameter associated with the integrated part of the model, which effects the amount of differencing to apply to a time series
- q is the parameter associated with the moving average part of the model
Let's buld the time series model using Auto Arima.