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Visar resultat 1 - 5 av 6 uppsatser som matchar ovanstående sökkriterier.
1. Probabilistic Forecasting through Reformer Conditioned Normalizing Flows
Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)Sammanfattning : Forecasts are essential for human decision-making in several fields, such as weather forecasts, retail prices, or stock predictions. Recently the Transformer neural network, commonly used for sequence-to-sequence tasks, has shown great potential in achieving state-of-the-art forecasting results when combined with density estimations models such as Autoregressive Flows. LÄS MER
2. Comparison of Indirect Inference and the Two Stage Approach
Kandidat-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)Sammanfattning : Parametric models are used to understand dynamical systems and predict its future behavior. It is difficult to estimate the model’s parametric values since there are usually many parameters and they are highly correlated. LÄS MER
3. Evaluation of established and new deep learning models for time series equity securities forecasting
Kandidat-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)Sammanfattning : In this bachelor thesis we investigate the importance of feature selection when making predictions on time series data. We compare how well different deep neural network models perform within equity securities time series prediction, namely the models RNN (Recurrent Neural Network), LSTM (Long Short Term Memory), LSTM with a peephole connection and last but not least GRU (Gated Recurrent Unit). LÄS MER
4. Trading with Artificial Neural Networks on Large-, Mid- and Small-Cap Stocks : Exploring if Market Cap has an effect on portfolio performance when trading with Artificial Neural Networks trained on historical stock data
Kandidat-uppsats, KTH/Skolan för datavetenskap och kommunikation (CSC)Sammanfattning : n this report one-day ahead stock prediction using artificial neural networks (ANN) is studied on stocks belonging to different market caps. Hennes & Mauritz, EnQuest PLC and Rottneros have been selected, representing large-, mid- and small-cap companies. LÄS MER
5. Examining how unforeseen events affect accuracy and recovery of a non-linear autoregressive neural network in stock market prognoses
Kandidat-uppsats, KTH/Skolan för datavetenskap och kommunikation (CSC)Sammanfattning : This report studies how a non-linear autoregressive neural network algorithm for stock market value prognoses is affected by unforeseen events. The study attempts to find out the recovery period for said algorithms after an event, and whether the magnitude of the event affects the recovery period. LÄS MER
