Sökning: "moving average"

Visar resultat 1 - 5 av 301 uppsatser innehållade orden moving average.

  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. Predicting Cryptocurrency Prices with Machine Learning Algorithms: A Comparative Analysis

    Kandidat-uppsats, Blekinge Tekniska Högskola/Institutionen för datavetenskap

    Författare :Harsha Nanda Gudavalli; Khetan Venkata Ratnam Kancherla; [2023]
    Nyckelord :Bitcoin; Cryptocurrency; Machine Learning;

    Sammanfattning : Background: Due to its decentralized nature and opportunity for substantial gains, cryptocurrency has become a popular investment opportunity. However, the highly unpredictable and volatile nature of the cryptocurrency market poses a challenge for investors looking to predict price movements and make profitable investments. LÄS MER