Sökning: "machine learning lstm"

Visar resultat 1 - 5 av 196 uppsatser innehållade orden machine learning lstm.

  1. 1. Physics-Enhanced Machine Learning for Energy Systems

    Uppsats för yrkesexamina på avancerad nivå, Lunds universitet/Institutionen för reglerteknik

    Författare :Henrik Lindström; Emil Sundström; [2022]
    Nyckelord :Technology and Engineering;

    Sammanfattning : Building operations account for a large amount of energy usage and the HVAC (Heating, Ventilation and Air Conditioning) systems are the largest consumer of energy in this sector. To reduce this demand, more energy-efficient control algorithms are implemented and a popular choice for a controller is the model predictive control. LÄS MER

  2. 2. Insurance Fraud Detection using Unsupervised Sequential Anomaly Detection

    Master-uppsats, Linköpings universitet/Institutionen för datavetenskap

    Författare :Anton Hansson; Hugo Cedervall; [2022]
    Nyckelord :Insurance Fraud Detection; Anomaly Detection; Long Short-Term Memory Networks LSTM ; Unsupervised Learning; Autoencoder AE ; Variational Autoencoder VAE ; Interpretable Machine Learning; Feature Engineering; Feature Selection; Feature Importance;

    Sammanfattning : Fraud is a common crime within the insurance industry, and insurance companies want to quickly identify fraudulent claimants as they often result in higher premiums for honest customers. Due to the digital transformation where the sheer volume and complexity of available data has grown, manual fraud detection is no longer suitable. LÄS MER

  3. 3. Predicting the Options Expiration Effect Using Machine Learning Models Trained With Gamma Exposure Data

    Kandidat-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Författare :Alexander Dubois; [2022]
    Nyckelord :AdaBoost; LSTM; Machine learning; Random forests; Stock markets; SVM;

    Sammanfattning : The option expiration effect is a well-studied phenome, however, few studies have implemented machine learning models to predict the effect on the underlying stock market due to options expiration. In this paper four machine learning models, SVM, random forest, AdaBoost, and LSTM, are evaluated on their ability to predict whether the underlying index rises or not on the day of option expiration. LÄS MER

  4. 4. Prediction of the number of weekly covid-19 infections : A comparison of machine learning methods

    Master-uppsats, Högskolan i Skövde/Institutionen för informationsteknologi

    Författare :Nicklas Branding; [2022]
    Nyckelord :Machine learning; deep learning; covid-19; public health science; number of infection; regression; long short term memory; gated recurrent unit; support vector regressor; long short term memory-convolutional neural network; bidirectional-long short term memory;

    Sammanfattning : The thesis two-folded problem aim was to identify and evaluate candidate Machine Learning (ML) methods and performance methods, for predicting the weekly number of covid-19 infections. The two-folded problem aim was created from studying public health studies where several challenges were identified. LÄS MER

  5. 5. Deep learning, LSTM and Representation Learning in Empirical Asset Pricing

    Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Författare :Benjamin von Essen; [2022]
    Nyckelord :LSTM; empirical asset pricing; deep learning; representation learning; neural networks; LSTM; empirisk tillgångsvärdering; djupinlärning; representationsinlärning; neurala nätverk;

    Sammanfattning : In recent years, machine learning models have gained traction in the field of empirical asset pricing for their risk premium prediction performance. In this thesis, we build upon the work of [1] by first evaluating models similar to their best performing model in a similar fashion, by using the same dataset and measures, and then expanding upon that. LÄS MER