# Time Series Cube Data Model

## TL;DR

This paper introduces a flexible, generic data model for time series data that ensures interoperability and extensibility across different scientific domains and future developments.

## Contribution

It presents a new, domain-independent time series data model and serialization standard that supports diverse data types and scientific use cases while maintaining backward compatibility.

## Key findings

- Defines building blocks for metadata discovery and data serialization.
- Supports various time series science cases with implementation examples.
- Ensures interoperability across different versions and domains.

## Abstract

The purpose of this document is to create a data model and its serialization for expressing generic time series data. Already existing IVOA data models are reused as much as possible. The model is also made as generic as possible to be open to new extensions but at the same time closed for modifications. This enables maintaining interoperability throughout different versions of the data model. We define the necessary building blocks for metadata discovery, serialization of time series data and understanding it by clients. We present several categories of time series science cases with examples of implementation. We also take into account the most pressing topics for time series providers like tracking original images for every individual point of a light curve or time-derived axes like frequency for gravitational wave analysis. The main motivation for the creation of a new model is to provide a unified time series data publishing standard - not only for light curves but also more generic time series data, e.g., radial velocity curves, power spectra, hardness ratio, provenance linkage, etc. The flexibility is the most crucial part of our model - we are not dependent on any physical domain or frame models. While images or spectra are already stable and standardized products, the time series related domains are still not completely evolved and new ones will likely emerge in near future. That is why we need to keep models like Time Series Cube DM independent of any underlying physical models. In our opinion, this is the only correct and sustainable way for future development of IVOA standards.