Essays about: "TimeGAN"
Showing result 1 - 5 of 7 essays containing the word TimeGAN.
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1. Restaurant Daily Revenue Prediction : Utilizing Synthetic Time Series Data for Improved Model Performance
University essay from Uppsala universitet/Avdelningen för beräkningsvetenskapAbstract : This study aims to enhance the accuracy of a demand forecasting model, XGBoost, by incorporating synthetic multivariate restaurant time series data during the training process. The research addresses the limited availability of training data by generating synthetic data using TimeGAN, a generative adversarial deep neural network tailored for time series data. READ MORE
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2. EVALUATING PERFORMANCE OF GENERATIVE MODELS FOR TIME SERIES SYNTHESIS
University essay from Mälardalens universitet/Akademin för innovation, design och teknikAbstract : Motivated by successes in the image generation domain, this thesis presents a novel Hybrid VQ-VAE (H-VQ-VAE) approach for generating realistic synthetic time series data with categorical features. The primary motivation behind this work is to address the limitations of existing generative models in accurately capturing the underlying structure and patterns of time series data, especially when dealing with categorical features. READ MORE
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3. Generative AI for Synthetic Data
University essay from Lunds universitet/Institutionen för reglerteknikAbstract : Synthetic data generation has emerged as a valuable technique for addressing data scarcity and privacy concerns and improving machine learning algorithms. This thesis focuses on progressing the field of synthetic data generation, which may play a crucial role in AI-heavy industries such as telecommunications. READ MORE
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4. Option Modelling by Deep Learning
University essay from Göteborgs universitet/Institutionen för nationalekonomi med statistikAbstract : In this thesis we aim to provide a fully data driven approach for modelling financial derivatives, exclusively using deep learning. In order for a derivatives model to be plausible, it should adhere to the principle of no-arbitrage which has profound consequences on both pricing and risk management. READ MORE
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5. Multivariate Time Series Data Generation using Generative Adversarial Networks : Generating Realistic Sensor Time Series Data of Vehicles with an Abnormal Behaviour using TimeGAN
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : Large datasets are a crucial requirement to achieve high performance, accuracy, and generalisation for any machine learning task, such as prediction or anomaly detection, However, it is not uncommon for datasets to be small or imbalanced since gathering data can be difficult, time-consuming, and expensive. In the task of collecting vehicle sensor time series data, in particular when the vehicle has an abnormal behaviour, these struggles are present and may hinder the automotive industry in its development. READ MORE
