Extracting statistics from sequential data
Time series data represents a sequence of measurements gathered over a certain period. These measurements are linked to a specific variable and are obtained at regular intervals. An essential characteristic of time series data is its inherent order, where the arrangement of observations along a timeline conveys significant information. Altering the sequence can completely change the data’s meaning. Sequential data, on a broader scale, encompasses any data presented sequentially, including time series data.
Our primary goal is to develop models that capture the underlying patterns within time series data or any sequential data. These models are instrumental in describing essential aspects of the time series patterns. They enable us to explore how past data influences the future, examine correlations between datasets, make future predictions, or control variables based on specific metrics. To visually represent time series data, we...
