Design and Applications of Machine Learning Algorithms for Time-Series Forecasting
type: CreativeWork
description: In the last decade, big data have gained extensive attention from both academia and industry. Machine learning has been introduced to learn from data, which plays a significant role in providing predictive analytical solutions for large-scale datasets. Therefore, the use of machine learning has increased dramatically during the last decade and it is continuously evolving and developing. In recent years, with the development of machine learning an extensive research has been carried out for solving real-world problems, such as self-driving cars, online recommendations, speech and face recognition systems, human activity recognition systems, and fraud detection in a banking system. One of the important subjects in machine learning is concerned with analyzing and forecasting time series datasets. Time series forecasting models help to predict future values of events having significant impact on our life, such as timely healthcare services and prediction of health conditions of a patient, forecasting the natural hazards and stock pries. Time series forecasting is an important avenue of research in machine learning due to many prediction problems that include a time variable. Nevertheless, when considering the time component as an additional information, it already makes time series problems more challenging to handle compared to other prediction tasks. In literature, most of the data-driven forecasting models are based on supervised learning algorithms. Nevertheless, those models are not time dependent and time variable t is not taken into account during the learning process. In practice, features of a time series dataset are time-variant and its corresponding output (target) value depends on time t. In predicting future time series data value, a historical data points should be used along with its corresponding time instances. Hence, the output of the forecasting model will then be a predicted value y(t0) at a time instance t0. In this regard, reliable and robust forec…
abstract: In the last decade, big data have gained extensive attention from both academia and industry. Machine learning has been introduced to learn from data, which plays a significant role in providing predictive analytical solutions for large-scale datasets. Therefore, the use of machine learning has increased dramatically during the last decade and it is continuously evolving and developing. In recent years, with the development of machine learning an extensive research has been carried out for solving real-world problems, such as self-driving cars, online recommendations, speech and face recognition systems, human activity recognition systems, and fraud detection in a banking system. One of the important subjects in machine learning is concerned with analyzing and forecasting time series datasets. Time series forecasting models help to predict future values of events having significant impact on our life, such as timely healthcare services and prediction of health conditions of a patient, forecasting the natural hazards and stock pries. Time series forecasting is an important avenue of research in machine learning due to many prediction problems that include a time variable. Nevertheless, when considering the time component as an additional information, it already makes time series problems more challenging to handle compared to other prediction tasks. In literature, most of the data-driven forecasting models are based on supervised learning algorithms. Nevertheless, those models are not time dependent and time variable t is not taken into account during the learning process. In practice, features of a time series dataset are time-variant and its corresponding output (target) value depends on time t. In predicting future time series data value, a historical data points should be used along with its corresponding time instances. Hence, the output of the forecasting model will then be a predicted value y(t0) at a time instance t0. In this regard, reliable and robust forec…
author: Avazov, Nurilla
license: https://researchspace.auckland.ac.nz/docs/uoa-docs/rights.htm
date published: 2021
date modified: 2026-07-24T01:02:59Z
provider: University of Auckland
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: ResearchSpace@Auckland

PropertyValue
property ID: Handle
value: https://hdl.handle.net/2292/57088