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static

TemporalScope/src/temporalscope/partition/single_target/static/init.py.

This module defines the namespace for static partitioning algorithms for single-target workflows in TemporalScope. Static partitioning refers to user-defined, fixed, and deterministic strategies, contrasting with dynamic (algorithmic or data-driven) methods. These methods are designed to work seamlessly with TimeFrame objects and support backend-agnostic operations via the Narwhals ecosystem.

Static Partitioning:

Static partitioning encompasses human-defined techniques, where partitions are specified based on domain knowledge or explicit rules. These methods are suited for: - Sliding Window Partitioning: Dividing data into fixed-size, non-overlapping or overlapping partitions. - Expanding Window Partitioning: Creating sequentially increasing partitions for cumulative analysis.

Single-Target Workflows:

This namespace supports DataFrame-centric workflows for scalar target models. Multi-target workflows, as described in the TemporalPartitionerProtocol, will require further extensions to ensure compatibility with TensorFlow/PyTorch datasets and sequence-target workflows.

Extensibility:

TemporalScope provides the foundation for users to implement or customize their own static partitioning strategies. This flexibility ensures the framework remains adaptable to diverse requirements and emerging techniques in time-series analysis.

Notes

Users are encouraged to leverage the TemporalPartitionerProtocol for building custom static partitioning workflows and refer to the foundational literature on partitioning techniques for guidance.

See Also
  1. Shah, A., DePavia, A., Hudson, N., Foster, I., & Stevens, R. (2024). Causal Discovery over High-Dimensional Structured Hypothesis Spaces with Causal Graph Partitioning. arXiv preprint arXiv:2406.06348.
  2. Nodoushan, A. N. (2023). Interpretability of Deep Learning Models for Time-Series Clinical Data. (Doctoral dissertation, The University of Arizona).
  3. Saarela, M., & Podgorelec, V. (2024). Recent Applications of Explainable AI (XAI): A Systematic Literature Review. Applied Sciences, 14(19), 8884.
  4. Nayebi, A., Tipirneni, S., Reddy, C. K., Foreman, B., & Subbian, V. (2023). WindowSHAP: An efficient framework for explaining time-series classifiers based on Shapley values. Journal of Biomedical Informatics, 144, 104438.
MODULE DESCRIPTION
sliding_window

TemporalScope/src/temporalscope/partition/single_target/sliding_window.py