Sökning: "Forecast Horizon"

Visar resultat 11 - 15 av 45 uppsatser innehållade orden Forecast Horizon.

1. 11. Photovoltaic System Performance Forecasting Using LSTM Neural Networks

   Master-uppsats, Uppsala universitet/Institutionen för informationsteknologi

   Författare :Lukas Hamberg; [2021]
   Nyckelord :Machine Learning; LSTM; neural networks; Photovoltaic systems; pv-systems; Deep learning; power output forecasting;

   Sammanfattning : Deep learning has proven to be a valued contributor to recent technological advancements within energy systems. This thesis project explores methods of photovoltaic (PV) system power output forecasting through the utilization of long short-term memory (LSTM) neural networks. LÄS MER

3. 12. Classifying stock returns using high-frequency fundamental factors and convolutional neural networks

   D-uppsats, Handelshögskolan i Stockholm/Institutionen för finansiell ekonomi

   Författare :Denis Dvinskikh; Axel Kotnik; [2021]
   Nyckelord :convolutional neural networks; fundamental factors; technical indicators; high-frequency stock prices; classification;

   Sammanfattning : We evaluate the usefulness of high-frequency fundamental factor exposures of five US equities, between 2013 and 2017, as features for classifying and predicting the binary movements of the same stocks in 5-minute and 20-day intervals using Convolutional Neural Networks (CNN). After plotting rolling factor betas (Market, HML, SMB) and the close price of a given stock in the corresponding intervals, these time series are converted into images as Gramian Angular Difference Fields (GADF) and then concatenated to be fed to the CNN as input. LÄS MER

4. 13. Comparison of Forecasting Models Used by The Swedish Social Insurance Agency.

   Magister-uppsats, Mälardalens högskola/Akademin för utbildning, kultur och kommunikation

   Författare :Ryan Rasoul; [2020]
   Nyckelord :Financial engineering; Forecast; Time series; ARIMA; SES; Analysis and forecasting;

   Sammanfattning : We will compare two different forecasting models with the forecasting model that was used in March 2014 by The Swedish Social Insurance Agency ("Försäkringskassan" in Swedish or "FK") in this degree project. The models are used for forecasting the number of cases. LÄS MER

5. 14. Long Term Forecasting of Industrial Electricity Consumption Data With GRU, LSTM and Multiple Linear Regression

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

   Författare :Roxana Buzatoiu; [2020]
   Nyckelord :Time Series Analysis; Recurrent Neural Networks; long-term Forecasting; Exploratory Data Analysis; Multiple Linear Regression; ACF; PACF; Energy Sector; Tidsserieanalys; återkommande neurala nätverk; långtidsprognoser; undersökande dataanalys; multipel linjär regression; ACF; PACF; energisektor;

   Sammanfattning : Accurate long-term energy consumption forecasting of industrial entities is of interest to distribution companies as it can potentially help reduce their churn and offer support in decision making when hedging. This thesis work presents different methods to forecast the energy consumption for industrial entities over a long time prediction horizon of 1 year. LÄS MER

7. 15. Sales Volume Forecasting of Ericsson Radio Units - A Statistical Learning Approach

   Master-uppsats, KTH/Matematisk statistik

   Författare :Patrik Amethier; André Gerbaulet; [2020]
   Nyckelord :Statistics; Applied mathematics; Neural networks; decision trees; machine learning; sales volumes; predictions; demand forecasting; Statistik; tillämpad matematik; neurala nätverk; trädmodeller; maskininlärning; försäljningsvolym; prognostisering;

   Sammanfattning : Demand forecasting is a well-established internal process at Ericsson, where employees from various departments within the company collaborate in order to predict future sales volumes of specific products over horizons ranging from months to a few years. This study aims to evaluate current predictions regarding radio unit products of Ericsson, draw insights from historical volume data, and finally develop a novel, statistical prediction approach. LÄS MER