Essays about: "multiple time series"

Showing result 36 - 40 of 152 essays containing the words multiple time series.

1. 36. Data Trustworthiness Assessment for Traffic Condition Participatory Sensing Scenario

   University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Author : Hairuo Gao; [2022]
   Keywords : Participatory sensing; Data trustworthiness assessment; Anomaly detection; Traffic prediction; Deep neural network; Deltagande avkänning; Bedömning av uppgifternas tillförlitlighet; Upptäckt av anomalier; Trafikprognoser; Djupt neuralt nätverk;

   Abstract : Participatory Sensing (PS) is a common mode of data collection where valuable data is gathered from many contributors, each providing data from the user’s or the device’s surroundings via a mobile device, such as a smartphone. This has the advantage of cost-efficiency and wide-scale data collection. READ MORE

3. 37. Imputing connections of random gene networks from time series data using ANNs

   University essay from Lunds universitet/Beräkningsbiologi och biologisk fysik - Genomgår omorganisation

   Author : Sofia Andersson; [2022]
   Keywords : artificial neural networks; ANNs; gene regulatory networks; GRNs; imputation; gene regulatory network imputation; GRN imputation; CNNs; convolutional neural network; randomly generated networks; ternary classification; binary classification; network inference; Physics and Astronomy;

   Abstract : This thesis presents the architecture of a convolutional neural network which is trained to impute the connections of randomly generated gene regulatory networks under varying amounts of regularisation. The generated gene networks are simulated from 10 different starting conditions for each set of connections in order to obtain multiple time series. READ MORE

4. 38. Forecasting the Nasdaq-100 index using GRU and ARIMA

   University essay from Uppsala universitet/Statistiska institutionen

   Author : David Cederberg; Daniel Tanta; [2022]
   Keywords : Neural network; GRU; ARIMA; stock market; forecasting; machine learning;

   Abstract : Today, there is an overwhelming amount of data that is being collected when it comes to financial markets. For forecasting stock indexes, many models rely only on historical values of the index itself. One such model is the ARIMA model. Over the last decades, machine learning models have challenged the classical time series models, such as ARIMA. READ MORE

5. 39. Attention-based Multi-Behavior Sequential Network for E-commerce Recommendation

   University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Author : Zilong Li; [2022]
   Keywords : Recommendation System; Sequential Recommendation; Click-through Rate Model; Transformer; Multi-Task Learning; Sistema di Raccomandazione; Raccomandazione Sequenziale; Modello di Percentuale di Clic; Trasformatore; Apprendimento Multitasking; Rekommendationssystem; Sekventiell rekommendation; Klickfrekvensmodell; Transformator; Multi-Task Learning;

   Abstract : The original intention of the recommender system is to solve the problem of information explosion, hoping to help users find the content they need more efficiently. In an e-commerce platform, users typically interact with items that they are interested in or need in a variety of ways. For example, buying, browsing details, etc. READ MORE

6. 40. Data-driven cyberattack detection for microgrids

   University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Author : Jiaying Mao; [2022]
   Keywords : Cyberattack detection; Deep learning network; Microgrid; Smart inverter; Cyber-physical system; Distributed system; Detektering av cyberattacker; Djupinlärning nätverk; Microgrid; Smart växelriktare; Cyberfysiska system; Distribuerade system;

   Abstract : Microgrids are undergoing higher penetrations of renewables and associated power electronics, along with precise and sophisticated control and communication networks. However, such cyber-physical systems might suffer from potential cybersecurity threats and inherent low inertia. READ MORE