Search for dissertations about: "machine learning and time series forecasting"

Showing result 1 - 5 of 8 swedish dissertations containing the words machine learning and time series forecasting.

2. 1. Scalable Streaming Graph and Time Series Analysis Using Partitioning and Machine Learning

   Author : Zainab Abbas; Vladimir Vlassov; Peter Van Roy; Paris Carbone; Vasiliki Kalavri; Vincenzo Massimiliano Gulisano; KTH; []
   Keywords : TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; Stream processing; graph processing; time series; big data; machine learning; Informations- och kommunikationsteknik; Information and Communication Technology;

   Abstract : Recent years have witnessed a massive increase in the amount of data generated by the Internet of Things (IoT) and social media. Processing huge amounts of this data poses non-trivial challenges in terms of the hardware and performance requirements of modern-day applications. READ MORE

4. 2. Privacy preserving behaviour learning for the IoT ecosystem

   Author : Sana Imtiaz; Vladimir Vlassov; Ramin Sadre; Sarunas Girdzijauskas; Omer Rana; KTH; []
   Keywords : TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; Internet of things; big data; privacy; smart health care; machine learning; synthetic data generation; generative adversarial networks; time-series data; distributed machine learning; Informations- och kommunikationsteknik; Information and Communication Technology;

   Abstract : IoT has enabled the creation of a multitude of personal applications and services for a better understanding and improvement of urban environments and our personal lives. These services are driven by the continuous collection and analysis of sensitive and private user data to provide personalised experiences. READ MORE

5. 3. Selected Topics in Mathematical Modelling: Machine Learning and Tugs-of-War

   Author : Carmina Fjellström; Kaj Nyström; Andrea Pascucci; Uppsala universitet; []
   Keywords : NATURVETENSKAP; NATURAL SCIENCES; Machine learning; Neural networks; LSTM; Financial forecasting; Time series analysis; Stochastic gradient descent; Diffusion map; Dimension reduction; Tug-of-war games; Fractional heat operator; Mean value property; Infinity fractional heat operators; Dynamic programming principle; p-Laplacian; Infinity Laplacian; Kolmogorov equation; Stochastic games; Viscosity solutions; Tillämpad matematik och statistik; Applied Mathematics and Statistics;

   Abstract : This thesis concerns selected topics in mathematical modelling, namely in machine learning and stochastic games called tugs-of-war. It consists of four scientific articles. The first and second are about machine learning topics, while the third and fourth articles are about tug-of-war games. READ MORE

6. 4. Multi-LSTM Acceleration and CNN Fault Tolerance

   Author : Stefano Ribes; Chalmers tekniska högskola; []
   Keywords : NATURVETENSKAP; NATURAL SCIENCES; TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; NATURVETENSKAP; NATURAL SCIENCES; TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; Compression; SVD; LSTMs; CNNs; Fault Tolerance; Machine Learning; FPGA; Roofline Model; HLS; Caffe;

   Abstract : This thesis addresses the following two problems related to the field of Machine Learning: the acceleration of multiple Long Short Term Memory (LSTM) models on FPGAs and the fault tolerance of compressed Convolutional Neural Networks (CNN). LSTMs represent an effective solution to capture long-term dependencies in sequential data, like sentences in Natural Language Processing applications, video frames in Scene Labeling tasks or temporal series in Time Series Forecasting. READ MORE

7. 5. Two-Level Multi-Objective Genetic Algorithm for Risk-Based Life Cycle Cost Analysis

   Author : Yamur K. Al-Douri; Uday Kumar; Jan Lundberg; Hussan Hamodi; Ahmed Al-Dubai; Luleå tekniska universitet; []
   Keywords : NATURVETENSKAP; NATURAL SCIENCES; TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; Artificial intelligence AI ; Life cycle cost LCC ; Machine learning ML ; Multi-objective genetic algorithm MOGA ; Risk-based life cycle cost LCC ; Tunnel fans; Two-level system; Operation and Maintenance Engineering; Drift och underhållsteknik;

   Abstract : Artificial intelligence (AI) is one of the fields in science and engineering and encompasses a wide variety of subfields, ranging from general areas (learning and perception) to specific topics, such as mathematical theorems. AI and, specifically, multi-objective genetic algorithms (MOGAs) for risk-based life cycle cost (LCC) analysis should be performed to estimate the optimal replacement time of tunnel fan systems, with a view towards reducing the ownership cost and the risk cost and increasing company profitability from an economic point of view. READ MORE