What is Time Series Analysis?
A time series analysis is a method of evaluating a collection of data points over some time. Instead of capturing data points sporadically or arbitrarily, time series analyzers capture data points at predictable intervals over a specified length of time. This sort of study, on the other hand, is more than just gathering data over time. The ability to demonstrate how variables change over time distinguishes time-series data from other types of data.
In other words, time is an important variable since it reveals how the data changes through time as well as the ultimate findings. It provides an extra source of data as well as a predetermined sequence of data dependencies. To achieve consistency and dependability, time series analysis generally requires a high number of data points. A large data collection guarantees that your sample size is representative and that your analysis can cut through noisy data.
The Need for Time Series Analysis
Organizations may utilize time series analysis to figure out what’s causing trends or systemic patterns across time. Business users may use data visualizations to observe seasonal trends and learn more about why they occur. These visualizations can now go much beyond line graphs thanks to current analytics tools. Organizations may use time series forecasting to anticipate the likelihood of future occurrences when they examine data at regular intervals.