A DETAILED-ON REVIEW TIME SERIES ANALYSIS AND DATA PROCESSING TECHNIQUES FOR ELECTRICAL ENGINEERING APPLICATIONS

Abstract

Time series analysis plays a critical role in modern electrical and electronics engineering for evaluating data trends, forecasting loads, validating measurements, and supporting system-level decision-making. As electrical systems become increasingly digitized, the availability of highresolution data from sensors, SCADA units, smart meters, and monitoring equipment necessitates robust data collection, sampling, and statistical analysis methods. This paper provides a comprehensive review of techniques used for validating electrical data, methods of data observation and collection, statistical approaches including t-tests, ANOVA, hypothesis testing, and strategies for interpretation. The study further examines advanced time series methods for electrical load forecasting, curve fitting, and interpolation techniques relevant to signal processing and power system analytics. Emphasis is placed on the integration of statistical software packages such as SigmaSTAT and SPSS in engineering workflows. The review highlights key challenges and emerging trends in analyzing electrical time series data for improved reliability, accuracy, and predictive performance.