Time series data is concerned with data instances in which each instance relates to a specific point in time or interval. How often we measure the variable of choice defines the time series' sampling frequency. For example, atmospheric temperature differs throughout the day and throughout the year. We can choose to measure the temperature every hour, so we have an hourly frequency, or we can choose to measure it each day, so we have a daily frequency. In finance, it is not unusual to have frequencies that are between major time intervals; this could be every 10 minutes (10m frequency) or every 4 hours (4h frequency). Another interesting characteristic of time series is that there is usually a correlation between instances that refer to proximal time points.
This is called autocorrelation. For example, the atmospheric temperature cannot vary by a great magnitude...
