Sökning: "Financial Time Series Forecasting"
Visar resultat 1 - 5 av 55 uppsatser innehållade orden Financial Time Series Forecasting.
1. Artificial Neural Networks for Financial Time Series Prediction
Master-uppsats, Stockholms universitet/Institutionen för data- och systemvetenskapSammanfattning : Financial market forecasting is a challenging and complex task due to the sensitivity of the market to various factors such as political, economic, and social factors. However, recent advances in machine learning and computation technology have led to an increased interest in using deep learning for forecasting financial data. LÄS MER
2. CryptoCurrency Time Series analysis : Comparative analysis between LSTM and BART Algorithm
Uppsats för yrkesexamina på grundnivå, Blekinge Tekniska Högskola/Institutionen för datavetenskapSammanfattning : 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
3. Credit Index Forecasting: Stability of an Autoregressive Model
Master-uppsats, KTH/Matematik (Avd.)Sammanfattning : This thesis investigates the robustness and stability of total return series for credit bond index investments. Dueto the challenges which arise for financial institutes and investors in achieving these objectives, we aim to createa forecasting model which matches the statistical properties of historical data, while remaining robust, stable andeasy to calibrate. LÄS MER
4. Machine Learning Based Stock Price Prediction by Integrating ARIMA model and Sentiment Analysis with Insights from News and Information
Kandidat-uppsats, Blekinge Tekniska Högskola/Institutionen för datavetenskapSammanfattning : Background: Predicting stock prices in today’s complex financial landscape is asignificant challenge. An innovative approach to address this challenge is integrating sentiment analysis techniques with the well-established Autoregressive IntegratedMoving Average (ARIMA) model. LÄS MER
5. LSTM-based Directional Stock Price Forecasting for Intraday Quantitative Trading
Kandidat-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)Sammanfattning : Deep learning techniques have exhibited remarkable capabilities in capturing nonlinear patterns and dependencies in time series data. Therefore, this study investigates the application of the Long-Short-Term-Memory (LSTM) algorithm for stock price prediction in intraday quantitative trading using Swedish stocks in the OMXS30 index from February 28, 2013, to March 1, 2023. LÄS MER