Time series data holds significant importance in digital analytics, particularly in areas like sales and demand analysis. Businesses can use time series analysis to analyse historical sales data and forecast future sales accurately. Forecasting future patterns and trends using historical data is extremely helpful for effective demand planning and inventory management.

However, to achieve precise sales forecasts, it is crucial to understand the historical data first. In the following section, we will explore various techniques using Jupyter Notebook to analyse an example time series data.

In time series data, each data point in the series is associated with specific time stamps, such as daily or weekly. This data serves the foundation for our analysis. (See the example below)

The first thing to do is to plot the time series and to see the overall development of sales over time as this is helpful to quickly spot upward or downward trends.

We can also decompose this time series to understand and extract the underlying components that contribute to its overall pattern. By breaking down the time series into its components, such as trend (the long-term direction or pattern), seasonality (patterns that repeat at fixed intervals), and residuals (remainder or noise).

To understand the seasonality factors of data we could slide and dice the data a bit further. For instance, by visualising sales data per year and quarter, we can easily identify recurring patterns and trends that may occur within each year. This helps in understanding the seasonal fluctuations and identifying quarters that generate the most of the sales.

As we see, visualising time series data before performing a forecast is crucial for understanding its patterns and trends. For example, we could further break down the sales by visualising the sales per weekday. This would help us to observe if any consistent fluctuations or seasonality effects are occurring every week. This insight enables us to identify if certain days of the week generate higher sales or if specific weeks throughout the year consistently perform better than others.

This knowledge and insights generated can serve as valuable inputs for time series forecasts, as the accuracy of the forecast can heavily rely on these inputs.