Wu, RuiMamua, Sharon Sone2023-06-052023-06-052023-052023-05-04May 2023http://hdl.handle.net/10342/12849Time series data is prevalent in many fields, such as finance, weather forecasting, and economics. Predicting future values of a time series can provide valuable insights for decision-making, such as identifying trends, detecting anomalies, and improving resource allocation. Recently, Generative Adversarial Networks (GANs) have been used to learn from these features to aid in time-series forecasting. We propose a novel framework that utilizes the unsupervised paradigm of a GAN based on related research called TimeGAN. Instead of using the discriminator as a classification model, we employ it as a regressive model to learn both temporal and static features. This framework can help generate synthetic data and facilitate forecasting. Our model outperforms TimeGAN, which only preserves temporal dynamics and uses the discriminator as a classifier to distinguish between synthetic and real datasets.application/pdfenRecurrent neural networksGenerative Adversarial NetworksForecastingTime-series analysisAlgorithmsArtificial intelligenceNeural networks (Computer science)Master's Thesis2023-06-02