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Welcome to the documentation of the TimeSeries library

TimeSeries is an Arduino library for time series forecasting.

The library provides support for well-known statistical forecasting models, such as autoregressive and moving average models. Recursive multi-step forecasting is supported.

Features

  • Dependency-free: at the moment, this library is completely dependency-free;
  • Dynamic-memory-free: every model provided by the library is implemented statically using templates and circular buffers;
  • Compatibility: as it makes no use of any board-specific instructions/features, this library should be compatible with almost every Arduino board;
  • Support for multi-step forecasting: the library supports recursive multi-step forecasting. Although only the recursive method is implemented directly, direct methods could of course be used by instantiating multiple models. See Sahoo et al. [1] for more details on these multi-step forecasting methods.

Documentation Guide

A quick and simple installation guide is available in our First Steps page.

For detailed information regarding every class, function, or method provided by the library, please refer to our API Reference.

Finally, be sure to check out our Examples page for guidance on how to use the library.

References

[1] Debashis Sahoo, Naveksha Sood, Usha Rani, George Abraham, Varun Dutt, and AD Dileep. Comparative analysis of multi-step time-series forecasting for network load dataset. In 2020 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT), pages 1–7. IEEE, 2020.