Time series analysis is a fundamental domain in data science and machine learning, with massive applications in various sectors such as e-commerce, finance, capacity planning, supply chain management, medicine, weather, energy, astronomy, and many others.
Time series analysis as a statistical technique is used to examine and model time-dependent data. Some common features of time series analysis tools include:
- Time series decomposition: the ability to break down a time series into its component parts, such as trend, seasonality, and residuals
- Forecasting: the ability to predict future values of a time series based on past data
- Anomaly detection: the ability to identify unusual or unexpected behavior in a time series
- Multivariate analysis: the ability to analyze multiple time series simultaneously, taking into account the relationships between them
- Feature extraction/embedding: the ability to extract meaningful features from time series data or to represent time series data in a lower-dimensional space for further analysis.
These are just a few examples of the types of functionality that may be included in a time series analysis tool. Let’s see what Kats can provide us with.