Sökning: "Hidden Markov model HMM"

Visar resultat 1 - 5 av 45 uppsatser innehållade orden Hidden Markov model HMM.

1. 1. Predictive Modeling and Statistical Inference for CTA returns : A Hidden Markov Approach with Sparse Logistic Regression

   Master-uppsats, Umeå universitet/Institutionen för matematik och matematisk statistik

   Författare :Oskar Fransson; [2023]
   Nyckelord :Probability theory; Statistical inference; finance; CTA; managed futures; machine learning; statistical learning; stochastic process; sparse logistic regression; Markov Chain Monte Carlo; Hidden Markov model;

   Sammanfattning : This thesis focuses on predicting trends in Commodity Trading Advisors (CTAs), also known as trend-following hedge funds. The paper applies a Hidden Markov Model (HMM) for classifying trends. Additionally, by incorporating additional features, a regularized logistic regression model is used to enhance prediction capability. LÄS MER

3. 2. Unsupervised Detection of Interictal Epileptiform Discharges in Routine Scalp EEG : Machine Learning Assisted Epilepsy Diagnosis

   Uppsats för yrkesexamina på avancerad nivå, Uppsala universitet/Avdelningen Vi3

   Författare :Shuai Shao; [2023]
   Nyckelord :EEG; electroencephalography; IED; interictal epileptiform discharges; spike detection; epilepsy; unsupervised; Fourier transform; STFT; short-time Fourier transform; CWT; continuous wavelet transform; DWT; discrete wavelet transform; ML; machine learning; ANN; artificial neural network; CNN; convolutional neural network; autoencoder; HMM; hidden Markov model; ECS; Euclidean distance of cumulative spectrum;

   Sammanfattning : Epilepsy affects more than 50 million people and is one of the most prevalent neurological disorders and has a high impact on the quality of life of those suffering from it. However, 70% of epilepsy patients can live seizure free with proper diagnosis and treatment. Patients are evaluated using scalp EEG recordings which is cheap and non-invasive. LÄS MER

4. 3. Reconstruction of Fire Spread with a Markov Random Field Mixture Model

   Master-uppsats, Lunds universitet/Matematisk statistik

   Författare :Marcus Gehrmann; [2023]
   Nyckelord :Forest fire; fire scars; spatial statistics; Markov random field; EM-algorithm; pseudo-likelihood; Mathematics and Statistics;

   Sammanfattning : This thesis revolves around reconstructing fire sizes for historical fires in Jämtgaveln, Sweden based on data of fire scars in trees. We propose a Hidden Markov Model (HMM), where the domain is divided into quadratic grid cells of 250 $\times$ 250 m and with these grid cells we associate a binary Markov random field taking values 0 or 1 corresponding to no fire and fire respectively. LÄS MER

5. 4. Model development of Time dynamic Markov chain to forecast Solar energy production

   Kandidat-uppsats, Linnéuniversitetet/Institutionen för matematik (MA)

   Författare :Angelica Bengtsson; [2023]
   Nyckelord :Markov chain; Time dynamic Markov chain; Hidden Markov model; Forecast;

   Sammanfattning : This study attempts to improve forecasts of solar energy production (SEP), so that energy trading companies can propose more accurate bids to Nord Pool. The aim ismake solar energy a more lucrative business, and therefore lead to more investments in this green energy form. LÄS MER

7. 5. GPS-Free UAV Geo-Localization Using a Reference 3D Database

   Master-uppsats, Linköpings universitet/Institutionen för systemteknik

   Författare :Justus Karlsson; [2022]
   Nyckelord :Deep Learning; Machine Learning; ML; AI; UAV; GPS-Free; CNN; 3D CNN; GCNN; 3D Database; geolocalization; geo-localization; georegistration; Hidden Markov Model; HMM; satellite; satellite database; Batch-Hard; triplet loss; PyTorch Geometric;

   Sammanfattning : The goal of this thesis has been global geolocalization using only visual input and a 3D database for reference. In recent years Convolutional Neural Networks (CNNs) have seen huge success in the task of classifying images. The flattened tensors at the final layers of a CNN can be viewed as vectors describing different input image features. LÄS MER