Sökning: "Tidsseriemodellering"

Hittade 5 uppsatser innehållade ordet Tidsseriemodellering.

1. 1. Football Trajectory Modeling Using Masked Autoencoders : Using Masked Autoencoder for Anomaly Detection and Correction for Football Trajectories

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

   Författare :Sandra Tor; [2023]
   Nyckelord :Machine Learning; Autoencoders; Masked autoencoders; Time series; Trajectory modeling; Time series modeling; Anomaly detection; Anomaly correction; Football; Maskininlärning; Autoencoders; Maskerade autoencoders; Tidsserie; Banmodellering; Tidsseriemodellering; Avvikelsedetektering; Avvikelsekorrigering; Fotboll;

   Sammanfattning : Football trajectory modeling is a powerful tool for predicting and evaluating the movement of a football and its dynamics. Masked autoencoders are scalable self-supervised learners used for representation learning of partially observable data. LÄS MER

3. 2. Improving Machinery Safety : Modelling data to explain machine stops and developing a strategy on how to reduce them

   Uppsats för yrkesexamina på avancerad nivå, Umeå universitet/Institutionen för matematik och matematisk statistik

   Författare :Kristina Leijonborg; Sandra Hammarsten; [2023]
   Nyckelord :;

   Sammanfattning : The purpose of this thesis is to examine how machinery safety at Stora Enso can be increased, with the goal of reducing the amount of machine stops and improving the operational safety within the Packaging Solutions division. To do this, data from the one of the machines at the Jönköping mill has been used for classification and time series modelling. LÄS MER

4. 3. Anomaly Detection for Temporal Data using Long Short-Term Memory (LSTM)

   Master-uppsats, KTH/Skolan för informations- och kommunikationsteknik (ICT)

   Författare :Akash Singh; [2017]
   Nyckelord :LSTM; RNN; anomaly detection; time series; deep learning; LSTM; RNN; avvikelsedetektion; tidsserier; djupt lärande;

   Sammanfattning : We explore the use of Long short-term memory (LSTM) for anomaly detection in temporal data. Due to the challenges in obtaining labeled anomaly datasets, an unsupervised approach is employed. We train recurrent neural networks (RNNs) with LSTM units to learn the normal time series patterns and predict future values. LÄS MER

5. 4. Tidsseriemodellering av fyra oreglerade älvars vattenföring - En explorativ studie med GARCH- och Tröskelteknik

   Kandidat-uppsats, Lunds universitet/Statistiska institutionen

   Författare :Dennis Pedersen; Henrik Bengtsson; [2016]
   Nyckelord :ARMA; GARCH; SETAR; tröskeleffekter; oreglerade älvar; vattenföring; Mathematics and Statistics;

   Sammanfattning : This paper studies the possibility to model the water flows of four Swedish unregulated rivers between 1980-2015 with ARMA, GARCH and SETAR models. By examining the amount of water that flows through the river and what the dependence in the process looks like in general, a further understanding for effects on for example wildlife and climate changes can be developed. LÄS MER

7. 5. Prediction of French day-ahead electricity prices: Comparison between a deterministic and a stochastic approach

   Master-uppsats, KTH/Matematisk statistik

   Författare :Léo André; [2015]
   Nyckelord :Electricity market; Market coupling; CWE area; Flow Base; modeling;

   Sammanfattning : This thesis deals with the new flow-based computation method used in the Central Western Europe Area. This is done on the financial side. The main aim is to produce some robust methods for predicting. LÄS MER