The authors propose a novel generative adversarial network (PCF-GAN) for generating high-fidelity time series data. The key contributions are:
Introducing the path characteristic function (PCF) as a principled representation of time series distribution, and using it to define a new distance metric (PCFD) to quantify the discrepancy between real and generated time series distributions.
Establishing the theoretical properties of PCFD, including characteristicity, boundedness, and differentiability, which ensure the stability and feasibility of training the PCF-GAN.
Designing efficient initialization and optimization schemes for the parameters of PCFD to strengthen its discriminative power and accelerate training efficiency.
Integrating an auto-encoder structure into the PCF-GAN, which provides additional reconstruction functionality for the generated time series.
The authors conduct extensive numerical experiments on various time series datasets, demonstrating that the PCF-GAN consistently outperforms state-of-the-art baselines in both generation and reconstruction quality.
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by Hang Lou,Sir... at arxiv.org 04-09-2024
https://arxiv.org/pdf/2305.12511.pdfDeeper Inquiries