Essays about: "Generering av Tidsseriedata"

Found 2 essays containing the words Generering av Tidsseriedata.

  1. 1. 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)

    Author : Sofia Nord; [2021]
    Keywords : Time Series Data Generation; Generative Adversarial Network; Deep Neural Network; Data Augmentation; Synthetic Data Generation; Generering av Tidsseriedata; Generativa Motstridande Nätverk; Djupa Neurala Nätverk; Dataökning; Syntetisk Datagenerering;

    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

  3. 2. Deep Scenario Generation of Financial Markets

    University essay from KTH/Matematisk statistik

    Author : Filip Carlsson; Philip Lindgren; [2020]
    Keywords : Variational Autoencoder; Generative Models; Latent Space; Dimensionality Reduction; Unsupervised Learning; Clustering; VAE-Clustering; Scenario Generation; Market Regime; Variational Autoencoder; generativa modeller; latent rum; dimensionsreducering; klustring; scenario generering;

    Abstract : The goal of this thesis is to explore a new clustering algorithm, VAE-Clustering, and examine if it can be applied to find differences in the distribution of stock returns and augment the distribution of a current portfolio of stocks and see how it performs in different market conditions. The VAE-clustering method is as mentioned a newly introduced method and not widely tested, especially not on time series. READ MORE