Sökning: "forecasting variance"

Visar resultat 21 - 25 av 49 uppsatser innehållade orden forecasting variance.

  1. 21. Gaussian Process Regression-based GPS Variance Estimation and Trajectory Forecasting

    Master-uppsats, Linköpings universitet/Institutionen för datavetenskap; Linköpings universitet/Tekniska fakulteten

    Författare :Linus Kortesalmi; [2018]
    Nyckelord :Machine Learning; GPR; Gaussian Process; GP; Gaussian Process Regression; Variance Estimation; Trajectory; Trajectory Forecasting; Regression; Gaussiska Processer; Variansestimering; trajektoria; Statistik; Maskininlärning;

    Sammanfattning : Spatio-temporal data is a commonly used source of information. Using machine learning to analyse this kind of data can lead to many interesting and useful insights. In this thesis project, a novel public transportation spatio-temporal dataset is explored and analysed. LÄS MER

  3. 22. Anomaly Detection in Time Series Data Based on Holt-Winters Method

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

    Författare :Adam Aboode; [2018]
    Nyckelord :data quality; anomaly detection; time series data; Holt-Winters method;

    Sammanfattning : In today's world the amount of collected data increases every day, this is a trend which is likely to continue. At the same time the potential value of the data does also increase due to the constant development and improvement of hardware and software. LÄS MER

  4. 23. High-variance multivariate time series forecasting using machine learning

    Master-uppsats, Uppsala universitet/Institutionen för informatik och media

    Författare :Nikola Katardjiev; [2018]
    Nyckelord :Data science; alcohol abuse; time series; forecastin; machine learning; deep learning; neural networks; regression; Data science; alkoholmissbruk; tidsserieanalys; prognos; maskininlärning; deep learning; neurala nätverk; regression;

    Sammanfattning : There are several tools and models found in machine learning that can be used to forecast a certain time series; however, it is not always clear which model is appropriate for selection, as different models are suited for different types of data, and domain-specific transformations and considerations are usually required. This research aims to examine the issue by modeling four types of machine- and deep learning algorithms - support vector machine, random forest, feed-forward neural network, and a LSTM neural network - on a high-variance, multivariate time series to forecast trend changes one time step in the future, accounting for lag. LÄS MER

  5. 24. Does High-Frequency Trading Affect Stock Market Predictability?

    Magister-uppsats, Lunds universitet/Nationalekonomiska institutionen

    Författare :Gustav Egesten; David Hasselström; [2018]
    Nyckelord :High-Frequency Trading; Predictability; Forecasting; ARMA; GARCH; Business and Economics;

    Sammanfattning : In this paper, it is investigated whether High-Frequency Trading has an impact on the stock market predictability or not, using nine different Autoregressive moving average models forecasts are generated. Thenceforth, ordinary least squares are used to regress the variance of the forecasting errors with High-Frequency Trading as an explanatory variable in order to see if it has any form of impact. LÄS MER

  7. 25. Parallel Bayesian Additive Regression Trees, using Apache Spark

    Master-uppsats, Uppsala universitet/Institutionen för informationsteknologi

    Författare :Sigurdur Geirsson; [2017]
    Nyckelord :;

    Sammanfattning : New methods have been developed to find patterns and trends in order to gainknowledge from large datasets in various disciplines, such as bioinformatics, consumer behavior in advertising and weather forecasting.The goal of many of these new methods is to construct prediction models from the data. LÄS MER