Towards Controllable Time Series Generation

arXiv:2403.03698v1 Announce Type: new
Abstract: Time Series Generation (TSG) has emerged as a pivotal technique in synthesizing data that accurately mirrors real-world time series, becoming indispensable in numerous applications. Despite significant advancements in TSG, its efficacy frequently hinges on having large training datasets. This dependency presents a substantial challenge in data-scarce scenarios, especially when dealing with rare or unique conditions. To confront these challenges, we explore a new problem of Controllable Time Series Generation (CTSG), aiming to produce synthetic time …

abstract applications arxiv challenge cs.ai cs.db cs.lg data datasets pivotal series time series training training datasets type world