Essays about: "Movement Prediction"
Showing result 1 - 5 of 66 essays containing the words Movement Prediction.
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1. On modelling OMXS30 stocks - comparison between ARMA models and neural networks
University essay from Uppsala universitet/Matematiska institutionenAbstract : This thesis compares the results of the performance of the statistical Autoregressive integrated moving average (ARIMA) model and the neural network Long short-term model (LSTM) on a data set, which represents a market index. Both models are used to predict monthly, daily, and minute close prices of the OMX Stockholm 30 Index. READ MORE
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2. A Markovian Approach to Financial Market Forecasting
University essay from KTH/Matematisk statistikAbstract : This thesis aims to investigate the feasibility of using a Markovian approach toforecast short-term stock market movements. To assist traders in making soundtrading decisions, this study proposes a Markovian model using a selection ofthe latest closing prices. READ MORE
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3. Electron-skyrmion systems, in and out of equilibrium, and isolated or contacted to reservoirs
University essay from Lunds universitet/Matematisk fysik; Lunds universitet/Fysiska institutionenAbstract : A Kondo lattice skyrmion model in contact with a macroscopic environment is simulated to explore skyrmion dynamics, which is an extension of previous work. The system is simulated using non-equilibrium Green's functions within the generalized Kadanoff-Baym ansatz and the wide band limit. READ MORE
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4. Improving Water Droplet Prediction for Vehicle Exterior Water Management: Insights from Experimental and Simulation Studies
University essay from KTH/Väg- och spårfordon samt konceptuell fordonsdesignAbstract : This thesis focuses on the study of water transportation on vehicle surfaces, which is crucial for ensuring the unobstructed operation of sensors and cameras in autonomous vehicles. The research aims to develop and validate experimental and simulation methods to enhance the understanding of water droplet behaviour and to create accurate models for computational fluid dynamics (CFD) simulations. READ MORE
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5. Temporal Localization of Representations in Recurrent Neural Networks
University essay from Högskolan Dalarna/Institutionen för information och teknikAbstract : Recurrent Neural Networks (RNNs) are pivotal in deep learning for time series prediction, but they suffer from 'exploding values' and 'gradient decay,' particularly when learning temporally distant interactions. Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU) have addressed these issues to an extent, but the precise mitigating mechanisms remain unclear. READ MORE