An Analysis of Time Series Analysis and Forecasting Techniques

Computer Engineering

Time series analysis and forecasting play a pivotal role in various domains, enabling informed decision-making and accurate predictions. This research paper conducts an in-depth analysis of diverse time series analysis and forecasting techniques, examining their efficacy, applicability, and interpretability. Through a systematic evaluation of established and emerging methods, we unveil their strengths and limitations across a spectrum of real-world scenarios. Leveraging empirical experiments and case studies, we present a comprehensive comparative assessment, shedding light on the performance of methods such as ARIMA, Exponential Smoothing, LSTM networks, and more. By scrutinizing the nuances of each technique, we illuminate the challenges posed by varying data characteristics and contexts. Moreover, we explore the evolving landscape of interpretability and explainability within time series forecasting, emphasizing the importance of transparent models in critical decision-making scenarios. As time series analysis continues to evolve, we chart a path toward future advancements, identifying open research questions and underscoring the need for innovative approaches to address persisting challenges. This research serves as a compass for practitioners and researchers, guiding them in selecting appropriate techniques and inspiring new avenues of exploration in the dynamic realm of time series analysis and forecasting.

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