So far you explored the two basic steps for forecasting – defining the problem and collecting the data. In this segment, you will learn the next step , that is , ‘Analyse the data’ by understanding the different components of the time series.
It seems like there are quite a few components associated with time series. Let’s look at them one by one once again.
- Level: This is the baseline of a time series. This gives the baseline to which we add the different other components.
- Trend: Over a longterm, this gives an indication of whether the time series moves lower or higher. For example, in the following Sensex graph you can clearly observe that with time, the overall value is increasing i.e. this particular time series data has an increasing trend.
- Seasonality: It is a pattern in a time-series data that repeats itself after a given period of time. For example, in the following graph ‘Monthly sales data of company X’, you can clearly observe that a fixed pattern is repeating every year. The simplest example to explain this could be, say, the sales of winter wear in India. In winter, during months like November-January, you would expect these sales to be very high whereas for the other months, the sales might be low. This shows a seasonality pattern and proves to be very useful when making forecasts.
- Cyclicity: It is also a repeating pattern in data that repeats itself aperiodically. We don’t get into the more details of this component as it is out of the scope of this module.
- Noise: Noise is the completely random fluctuation present in the data and we cannot use this component to forecast into the future. This is that component of the time series data that no one can explain and is completely random.
Now, in the next segment, let’s explore the air passenger traffic dataset and understand all the time series components it has.