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PreprintCC BY-SA 4.0arXiv

Scentree: a framework for generating scenario trees for multistage stochastic programming

  • Cristian Pachón-García
  • Albert Solà Vilalta
  • F-. Javier Heredia
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Abstract

We present scentree, an open-source Python package for constructing a scenario fan and a scenario tree for multistage stochastic programming from historical data. It combines machine learning and multivariate time series models to obtain a scenario fan that captures inter-stage dependencies in the stochastic processes. This scenario fan is subsequently transformed into a scenario tree suitable for multistage stochastic optimization, providing a flexible and extensible framework for uncertainty modeling. A key contribution is the automation of the complete workflow, including model selection, parameter estimation, scenario fan generation, and scenario tree construction. Scentree does not rely on assumptions about the underlying data distribution, reducing the statistical expertise required to produce a scenario tree. Furthermore, it is agnostic to the specific multistage stochastic problem to be solved.

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Cite

BibTeXRIS
BibTeX
@misc{pachongarcia2026scentree,
  title = {{Scentree: a framework for generating scenario trees for multistage stochastic programming}},
  author = {Cristian Pachón-García and Albert Solà Vilalta and F-. Javier Heredia},
  howpublished = {arXiv},
  year = {2026},
  month = sep,
  doi = {10.48550/arxiv.2609.21495},
  eprint = {2609.21495},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2609.21495},
  note = {CC BY-SA 4.0},
}
RIS
TY  - UNPB
TI  - Scentree: a framework for generating scenario trees for multistage stochastic programming
AU  - Cristian Pachón-García
AU  - Albert Solà Vilalta
AU  - F-. Javier Heredia
PY  - 2026
DA  - 2026/09/18
PB  - arXiv
DO  - 10.48550/arxiv.2609.21495
UR  - https://arxiv.org/abs/2609.21495
AB  - We present scentree, an open-source Python package for constructing a scenario fan and a scenario tree for multistage stochastic programming from historical data. It combines machine learning and multivariate time series models to obtain a scenario fan that captures inter-stage dependencies in the stochastic processes. This scenario fan is subsequently transformed into a scenario tree suitable for multistage stochastic optimization, providing a flexible and extensible framework for uncertainty modeling. A key contribution is the automation of the complete workflow, including model selection, parameter estimation, scenario fan generation, and scenario tree construction. Scentree does not rely on assumptions about the underlying data distribution, reducing the statistical expertise required to produce a scenario tree. Furthermore, it is agnostic to the specific multistage stochastic problem to be solved.
ER  -