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Open Access Journal Article

Statistical Methods for Forecasting in Dynamic Environments

by Daniel Martin 1,*
1
Daniel Martin
*
Author to whom correspondence should be addressed.
ORS  2023 5(1):37; https://doi.org/10.69610/j.ors.20230316
Received: 26 January 2023 / Accepted: 23 February 2023 / Published Online: 16 March 2023
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Abstract

This paper explores the application of statistical methods in forecasting within dynamic environments, where market conditions, consumer behavior, and external factors are subject to rapid change. The study aims to provide insights into the effectiveness and efficiency of various statistical techniques in predicting future trends and outcomes under such uncertainty. By reviewing existing literature and conducting empirical analyses, the paper identifies key challenges and proposes innovative approaches for handling the complexities associated with dynamic environments. The analysis focuses on time-series forecasting, machine learning algorithms, and ensemble methods, demonstrating how these tools can be adapted to account for the volatility and unpredictability of dynamic systems. Additionally, the paper discusses the importance of incorporating real-time data and adaptive models to enhance forecasting accuracy. The findings suggest that a combination of traditional statistical methods and advanced machine learning techniques can significantly improve the reliability and relevance of forecasts in dynamic environments.

Keywords: Statistical Forecasting; Dynamic Environments; Time-Series Analysis; Machine Learning; Ensemble Methods.;
Copyright: © 2023 by Martin. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) (Creative Commons Attribution 4.0 International License). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Cite This Paper
APA Style
Martin, D. (2023). Statistical Methods for Forecasting in Dynamic Environments. Operations Research and Statistics, 5(1), 37. doi:10.69610/j.ors.20230316
ACS Style
Martin, D. Statistical Methods for Forecasting in Dynamic Environments. Operations Research and Statistics, 2023, 5, 37. doi:10.69610/j.ors.20230316
AMA Style
Martin D. Statistical Methods for Forecasting in Dynamic Environments. Operations Research and Statistics; 2023, 5(1):37. doi:10.69610/j.ors.20230316
Chicago/Turabian Style
Martin, Daniel 2023. "Statistical Methods for Forecasting in Dynamic Environments" Operations Research and Statistics 5, no.1:37. doi:10.69610/j.ors.20230316

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ACS Style
Martin, D. Statistical Methods for Forecasting in Dynamic Environments. Operations Research and Statistics, 2023, 5, 37. doi:10.69610/j.ors.20230316
AMA Style
Martin D. Statistical Methods for Forecasting in Dynamic Environments. Operations Research and Statistics; 2023, 5(1):37. doi:10.69610/j.ors.20230316
Chicago/Turabian Style
Martin, Daniel 2023. "Statistical Methods for Forecasting in Dynamic Environments" Operations Research and Statistics 5, no.1:37. doi:10.69610/j.ors.20230316
APA style
Martin, D. (2023). Statistical Methods for Forecasting in Dynamic Environments. Operations Research and Statistics, 5(1), 37. doi:10.69610/j.ors.20230316

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