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.

翻译：在时间序列编辑中，我们的目标是在不改变其他属性的前提下，修改给定时间序列的某些特性。例如，在分析医院患者的血压数据时，我们可能希望添加一个早期的突然下降，并在保持其他条件不变的情况下观察其对未来的影响。现有的基于扩散的编辑器依赖于僵化的、预定义的属性向量作为条件，并通过采样产生“全有或全无”的编辑。这种基于属性和采样的方法在条件格式上缺乏灵活性，并且无法对编辑强度进行可定制的控制。为了克服这些限制，我们提出了基于指令的时间序列编辑，用户可以使用自然语言指定预期的编辑内容。这允许用户以更易访问的格式表达更广泛的编辑类型。接着，我们提出了InstructTime，这是首个基于指令的时间序列编辑器。InstructTime接收时间序列和指令，将它们嵌入到一个共享的多模态表示空间中，然后解码这些嵌入以生成编辑后的时间序列。通过学习结构化的多模态表示空间，我们可以轻松地在嵌入之间进行插值，以实现不同程度的编辑。为了同时处理局部和全局编辑，我们提出了多分辨率编码器。在我们的实验中，我们使用了合成和真实数据集，并发现InstructTime是一种最先进的时间序列编辑器：InstructTime能够以可控的强度实现高质量的编辑，可以泛化到未见过的指令，并且能够通过少样本学习轻松适应未见过的条件。