Time-series data

What Time-series data is

Time-series data is a sequence of data points that are collected over a period of time. This type of data is often used to analyze trends over time to identify patterns and make predictions.

Steps for Time-series Data:

1. Collect data: Gather the data points that need to be analyzed and stored in a time series database.
2. Analyze data: Use statistical methods such as autocorrelation, seasonality, and lagging to identify patterns and trends in the data.
3. Forecast data: Create models to predict future values of the time series data.
4. Monitor data: Monitor the data over time to ensure that the models are performing as expected.
5. Update data: Update the time series database as new data points are collected.

Examples

1. Temperature readings over a period of time
2. Stock market prices over a period of time
3. Web traffic over a period of time
4. Sales data over a period of time
5. Electricity usage over a period of time
6. Heart rate over a period of time
7. Air quality readings over a period of time
8. Rainfall data over a period of time
9. Pollution levels over a period of time
10. CPU utilization over a period of time