A SURVEY ON FORECASTING IOT TIME SERIES DATA

Abstract

The rapid growth of technologies such as sensors and the Internet of Things (IoT) has led to the continuous generation of vast amounts of data in the form of time series. Over the years, analyzing and forecasting time series data has become a central area of research due to its wide range of applications. Accurate forecasting plays a vital role in domains such as business, financial markets, weather prediction, electricity demand estimation, and resource management, including the consumption of fuels and power. It is also critical in any domain influenced by seasonal variations or long-term trends. Reliable forecasts provide organizations with essential insights for informed decision-making and strategic planning.

This paper presents a comprehensive survey of the techniques employed for time series forecasting across different types of datasets. It reviews general forecasting models, the algorithms underpinning these models, and optimization strategies developed to enhance predictive performance and accuracy. In addition, the paper highlights the evaluation metrics commonly applied to assess the effectiveness of forecasting approaches. By synthesizing prior research, this study offers a consolidated understanding of the advancements in time series forecasting and serves as a reference point for researchers and practitioners working in this area.