Sökning: "Autoregressive Models"
Visar resultat 1 - 5 av 179 uppsatser innehållade orden Autoregressive Models.
1. Predicting Electricity Consumption with ARIMA and Recurrent Neural Networks
Kandidat-uppsats, Uppsala universitet/Statistiska institutionenSammanfattning : Due to the growing share of renewable energy in countries' power systems, the need for precise forecasting of electricity consumption will increase. This paper considers two different approaches to time series forecasting, autoregressive moving average (ARMA) models and recurrent neural networks (RNNs). LÄS MER
2. Spatio-temporal analysis of COVID-19 in Västra Götaland, Sweden
Master-uppsats, Göteborgs universitet/Institutionen för matematiska vetenskaperSammanfattning : Spatio-temporal analysis of COVID-19 data with the two different statistical approaches is the main objective of this thesis. The first classical approach, the Endemic-Epidemic framework (Held et al., 2005) is a class of multivariate time-series models for the incidence counts, obtained from the surveillance systems. LÄS MER
3. Estimating historic ranges of extinct scavenging birds from North America during the late Pleistocene using co-occurrence data from the fossil record
Master-uppsats, Göteborgs universitet / Institutionen för biologi och miljövetenskapSammanfattning : The aim of my study was to estimate and compare the historic range of nine scavenging birds from North America that went jointly extinct with their mammalian megafaunal prey in the Late Pleistocene. Although the severity and timing of their co-extinction are strongly correlated, there has been little analytical support in providing estimates for the possible geographic distribution of scavenging birds prior to the extinction event. LÄS MER
4. Forecasting Volatility of Electricity Intraday Log Returns with Generalized Autoregressive Score Models
Master-uppsats, Göteborgs universitet/Graduate SchoolSammanfattning : We forecast volatility of electricity intraday log returns with Generalized Autoregressive Score (GAS) models. We extend our GAS models with variables representing the difference between the public’s expectation of weather and energy load and the actual outcome using a restricted ARMA(4,4) model. LÄS MER
5. Evaluating Brain-Inspired Machine Learning Models for Time Series Forecasting: A Comparative Study on Dynamical Memory in Reservoir Computing and Neural Networks
Kandidat-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)Sammanfattning : Brain-inspired computing is a promising research field, with potential to encouragebreakthroughs within machine learning and enable us to solve complex problems in a moreefficient way. This study aims to compare the performance of brain-like machine learningalgorithms for time series forecasting. LÄS MER