Cross-sectional data observes individuals and companies in the same time period. On the other hand, time-series data is any data set that collects information at various time intervals. This data is distinct because it orders data points by time. As a result, there is potential for correlation between observations in adjacent intervals.
Time-series data is plottable on a graph with incremental intervals (or timelines) on the x-axis and observed sample data values on the y-axis. Such time-series graphs are valuable tools for visualizing the data. Data scientists use them to identify forecasting data characteristics. We give some examples of time-series data characteristics below:
Time trending data
In trending data, y-values increase or decrease with time, making the graph appear linear. For example, population data may increase or decrease linearly with time.
Seasonality
Seasonal patterns occur when time-series data shows regular and predictable patterns at time intervals of less than a year. This data pattern may appear as spikes or other anomalies on an otherwise linear graph. For example, a store’s retail sales might increase in the holiday periods around December and April.
Structural breaks
Sometimes time-series data suddenly changes behavior at a certain point in time. The time-series graph may suddenly shift up or down, creating a structural break or non-linearity. For instance, many economic indicators changed sharply in 2008 after the start of the global financial crisis.