Time-Series Analysis: What Is It and How to Use It

Time-series analysis is a statistical technique that deals with time-series data or trend analysis. It involves the identification of patterns, trends, seasonality, and irregularities in the data observed over different periods. Key methodologies used in time-series analysis include moving averages, exponential smoothing, decomposition methods, and Autoregressive Integrated Moving Average (ARIMA) models. Time-series analysis is commonly used to analyze trends, patterns, and behaviors over time in various fields such as finance, healthcare, energy consumption, manufacturing, supply chain management, web traffic, and user behavior. The four components of time-series analysis are trend, seasonality, cyclicity, and irregularity. There are five types of time-series analysis: exploratory analysis, curve fitting, forecasting, classification, and segmentation. Time-series visualization can be done using run charts, overlapping charts, or separated charts.