What Is Time-Series Analysis? (With Examples and Applications)

Time-series analysis is a process used to examine time-series data to understand patterns, trends, seasonality, and anomalies. It involves decomposing the data into four components: trend, seasonality, cyclicity, and irregularity. Time-series analysis can be applied to various fields such as finance, healthcare, and IoT devices. The goal of time-series analysis is to extract insights from historical data to make predictions about future events or trends. This type of analysis can help in understanding the "bigger picture" of a series, making it easier to identify patterns and anomalies. It also enables the use of machine learning algorithms to forecast future values and make informed decisions. Time-series analysis has various types such as exploratory analysis, curve fitting, forecasting, classification, segmentation, visualization, and more. Each type of analysis serves a specific purpose in extracting insights from time-series data.