Instruction-based Time Series Editing

TL;DR

This paper introduces InstructTime, a novel instruction-based time series editing method that allows natural language commands to control edits, offering flexible, high-quality, and customizable modifications for both synthetic and real datasets.

Contribution

We propose InstructTime, the first instruction-based time series editor that embeds instructions and time series into a shared space for flexible, controllable editing.

Findings

InstructTime achieves state-of-the-art editing quality.

It generalizes well to unseen instructions.

It allows interpolation for varying edit strengths.

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 space, we can easily interpolate between embeddings to achieve varying degrees of edit. To handle local and global edits together, we propose multi-resolution encoders. In our experiments, we use synthetic and real datasets and find that InstructTime is a state-of-the-art time series editor: InstructTime achieves high-quality edits with controllable strength, can generalize to unseen instructions, and can be easily adapted to unseen conditions through few-shot learning.