Milestones and developments in classical statistical time series forecasting: a comprehensive review
DOI:
https://doi.org/10.15282/daam.v6i1.12113Keywords:
Time series, Forecasting, Univariate, Methods, ApplicationsAbstract
Time series offers a potent framework for forecasting the future based on past data. Time series forecasting, a cornerstone of predictive analytics, is essential to decision-making in many fields, including finance, economics, weather, and healthcare, necessitating various forecasting methodologies. In the 1950s, statistical tools for forecasting began utilising exponential smoothing methods. These approaches were changed depending on the pattern seen in the data sets and the objective of the analysis. Automated functions were used after simple additive and multiplicative effects to assess the complexity of the data for forecasting purposes. This paper thoroughly reviewed the developments and turning points in classical time series forecasting techniques. Time series analysis has long been built on classical forecasting methods, shaping the field's growth. A thorough examination of the literature and historical data has identified noteworthy advancements that have significantly influenced the area. The review emphasises how classical approaches can still be helpful in today's data-driven society and how they can be combined with cutting-edge data science methodologies. This review also provides insightful analyses into the lengthy past and bright future of time series forecasting by charting the development of conventional approaches.
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