Time-series graphs are powerful tools for visualizing data sequenced by time, allowing trends and irregularities to become apparent without sifting through raw data. These graphs, such as line, multi-line, smooth interpolation, area, dual-axis, and scatter plots, are used in various applications like time-series decomposition, model building, and forecasting. The article highlights how time-series data, which does not have to occur at regular intervals, can be effectively visualized with different types of graphs to emphasize specific data features. It also discusses the tools and methods for creating time-series graphs, including spreadsheet editors like Excel and Google Sheets, programming languages like Python and R, and SQL databases, with a particular focus on Timescale and Grafana for robust time-series analysis. The importance of understanding the distinctions among graphs, charts, and plots is emphasized to ensure clarity in data representation.