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[2508.01504] Instruction-based Time Series Editing
Summary
The paper introduces Instruction-based Time Series Editing, a novel approach that allows users to modify time series data using natural language instructions, enhancing flexibility and control over edits.
Why It Matters
This research addresses the limitations of existing time series editing methods by enabling more intuitive and customizable edits. It has significant implications for fields like healthcare, finance, and any domain relying on time series data analysis, facilitating better decision-making and insights.
Key Takeaways
- Introduces InstructTime, the first instruction-based time series editor.
- Allows users to specify edits using natural language, increasing accessibility.
- Achieves high-quality edits with controllable strength and generalizes to unseen instructions.
- Utilizes multi-resolution encoders for handling local and global edits.
- Demonstrates effectiveness through experiments on synthetic and real datasets.
Computer Science > Machine Learning arXiv:2508.01504 (cs) [Submitted on 2 Aug 2025 (v1), last revised 13 Feb 2026 (this version, v4)] Title:Instruction-based Time Series Editing Authors:Jiaxing Qiu, Dongliang Guo, Brynne Sullivan, Teague R. Henry, Thomas Hartvigsen View a PDF of the paper titled Instruction-based Time Series Editing, by Jiaxing Qiu and 4 other authors View PDF HTML (experimental) Abstract:In time series editing, we aim to modify some properties of a given time series without altering others. For example, when analyzing a hospital patient's blood pressure, we may add a sudden early drop and observe how it impacts their future while preserving other conditions. Existing diffusion-based editors rely on rigid, predefined attribute vectors as conditions and produce all-or-nothing edits through sampling. This attribute- and sampling-based approach limits flexibility in condition format and lacks customizable control over editing strength. To overcome these limitations, we introduce Instruction-based Time Series Editing, where users specify intended edits using natural language. This allows users to express a wider range of edits in a more accessible format. We then introduce InstructTime, the first instruction-based time series editor. InstructTime takes in time series and instructions, embeds them into a shared multi-modal representation space, then decodes their embeddings to generate edited time series. By learning a structured multi-modal representation spac...
