Sökning: "Arima models"

Visar resultat 1 - 5 av 113 uppsatser innehållade orden Arima models.

1. 1. Predicting Electricity Consumption with ARIMA and Recurrent Neural Networks

   Kandidat-uppsats, Uppsala universitet/Statistiska institutionen

   Författare :Klara Enerud; [2024]
   Nyckelord :time series forecasting; ARIMA; recurrent neural networks; LSTM; electricity forecasting; EED forecasting;

   Sammanfattning : 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

3. 2. Demand Forecasting of Automobile Spare Parts after the End-of-Production - A review of demand forecasting models

   Master-uppsats, Göteborgs universitet/Graduate School

   Författare :Abid Ali; Arosha Ratnayake; [2023-07-03]
   Nyckelord :Demand forecasting; Spare parts; Automobile; End-of-Production EOP ; PRISMA; AHP; MCDM;

   Sammanfattning : Demand forecasting of spare parts plays a crucial role in automobile industry where it generally requires a significant attention in controlling inventory. It is possible to maintain an optimal stock level when there is a continues supply at the Original Equipment Manufacturers (OEMs). LÄS MER

4. 3. 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)

   Författare :Eddie Nevander Hellström; Johan Slettengren; [2023]
   Nyckelord :;

   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

5. 4. On modelling OMXS30 stocks - comparison between ARMA models and neural networks

   Master-uppsats, Uppsala universitet/Matematiska institutionen

   Författare :Irina Zarankina; [2023]
   Nyckelord :ARMA; ARIMA; LSTM; time series; statistics;

   Sammanfattning : 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. LÄS MER

7. 5. CryptoCurrency Time Series analysis : Comparative analysis between LSTM and BART Algorithm

   Uppsats för yrkesexamina på grundnivå, Blekinge Tekniska Högskola/Institutionen för datavetenskap

   Författare :Lakshmi Vyshnavi Nerella; Chiranjeevi Ponnada; [2023]
   Nyckelord :;

   Sammanfattning : Background: Cryptocurrency is an innovative digital or virtual form of money thatuses cryptographic techniques for secured financial transactions within a decentralized structure. Due to its high volatility and susceptibility to external factors, itis difficult to understand its behavior which makes accurate predictions challengingfor the investors who are trying to forecast price changes and make profitable investments. LÄS MER