Sökning: "Long Short-Term Memory LSTM"

Visar resultat 11 - 15 av 264 uppsatser innehållade orden Long Short-Term Memory LSTM.

1. 11. Aktiemarknadsprognoser: En jämförande studie av LSTM- och SVR-modeller med olika dataset och epoker

   Kandidat-uppsats, Malmö universitet/Fakulteten för teknik och samhälle (TS)

   Författare :Mads Nørklit Johansen; Jagtej Sidhu; [2023]
   Nyckelord :Stock Market Prediction; Long-Short Term Memory; Support Vector Regression; Prediction Accuracy; Financial Investments;

   Sammanfattning : Predicting stock market trends is a complex task due to the inherent volatility and unpredictability of financial markets. Nevertheless, accurate forecasts are of critical importance to investors, financial analysts, and stakeholders, as they directly inform decision-making processes and risk management strategies associated with financial investments. LÄS MER

3. 12. Modelling Proxy Credit Cruves Using Recurrent Neural Networks

   Master-uppsats, KTH/Matematisk statistik

   Författare :Lucas Fageräng; Hugo Thoursie; [2023]
   Nyckelord :Deep Neural Networks; Credit Risk; Financial Modelling; LSTM; Credit Default Swaps; Credit Valuation Adjustment; Djupa Neurala Nätverk; Kreditrisk; Finansiell Modellering; LSTM; Kreditswappar; Kreditvärderingsjustering;

   Sammanfattning : Since the global financial crisis of 2008, regulatory bodies worldwide have implementedincreasingly stringent requirements for measuring and pricing default risk in financialderivatives. Counterparty Credit Risk (CCR) serves as the measure for default risk infinancial derivatives, and Credit Valuation Adjustment (CVA) is the pricing method used toincorporate this default risk into derivatives prices. LÄS MER

4. 13. Predicting Cryptocurrency Prices with Machine Learning Algorithms: A Comparative Analysis

   Kandidat-uppsats, Blekinge Tekniska Högskola/Institutionen för datavetenskap

   Författare :Harsha Nanda Gudavalli; Khetan Venkata Ratnam Kancherla; [2023]
   Nyckelord :Bitcoin; Cryptocurrency; Machine Learning;

   Sammanfattning : Background: Due to its decentralized nature and opportunity for substantial gains, cryptocurrency has become a popular investment opportunity. However, the highly unpredictable and volatile nature of the cryptocurrency market poses a challenge for investors looking to predict price movements and make profitable investments. LÄS MER

5. 14. Exploration and Evaluation of RNN Models on Low-Resource Embedded Devices for Human Activity Recognition

   Master-uppsats, KTH/Mekatronik och inbyggda styrsystem

   Författare :Helgi Hrafn Björnsson; Jón Kaldal; [2023]
   Nyckelord :Recurrent Neural Networks; Long Short-Term Memory Networks; Embedded Systems; Human Activity Recognition; Edge AI; TensorFlow Lite Micro; Recurrent Neural Networks; Long Short-Term Memory Networks; Innbyggda systyem; Mänsklig aktivitetsigenkänning; Edge AI; TensorFlow Lite Micro;

   Sammanfattning : Human activity data is typically represented as time series data, and RNNs, often with LSTM cells, are commonly used for recognition in this field. However, RNNs and LSTM-RNNs are often too resource-intensive for real-time applications on resource constrained devices, making them unsuitable. LÄS MER

7. 15. Customer churn prediction in a slow fashion e-commerce context : An analysis of the effect of static data in customer churn prediction

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

   Författare :Luca Colasanti; [2023]
   Nyckelord :Survival Analysis; Time To Event prediction; Churn retention; Machine Learning; Deep Learning; Customer Clustering; E-commerce; Analisi di sopravvivenza; Previsione del tempo a evento; Ritenzione dall’abbandono dei clienti; Apprendimento automatico; Apprendimento profondo; Segmentazione della clientela; Commercio elettronico; Överlevnadsanalys; Tid till händelseförutsägelse; Churn Prediction; Maskininlärning; Djuplärning; Kundkluster; E-handel;

   Sammanfattning : Survival analysis is a subfield of statistics where the goal is to analyse and model the data where the outcome is the time until the occurrence of an event of interest. Because of the intrinsic temporal nature of the analysis, the employment of more recently developed sequential models (Recurrent Neural Network (RNN) and Long Short Term Memory (LSTM)) has been paired with the use of dynamic temporal features, in contrast with the past reliance on static ones. LÄS MER