The field of time series analysis is witnessing a significant shift towards the development of innovative simulation platforms and causal modeling techniques. Researchers are focusing on creating interactive frameworks that can generate synthetic multivariate time series data with known causal dynamics, allowing for the validation and benchmarking of causal discovery algorithms. Additionally, there is a growing interest in rethinking time series generation from a graph-based perspective, enabling the capture of complex temporal dependencies and structural patterns. Furthermore, advancements in semi-Markovian structural causal models are being explored, which can represent confounding relationships that were previously challenging to model. Noteworthy papers in this area include: KarmaTS, which introduces an interactive framework for constructing lag-indexed, executable spatiotemporal causal graphical models for multivariate time series simulation. TSGDiff, which presents a novel framework that rethinks time series generation from a graph-based perspective, generating high-quality synthetic time series data that faithfully preserve temporal dependencies and structural integrity.