Descriptive and predictive time series analysis serve different purposes in understanding and forecasting data over time. Descriptive analysis focuses on summarizing past data to identify patterns, trends, and other characteristics. It answers questions like "What has happened?" or “What are the underlying patterns?” For instance, a developer might analyze website traffic data to determine peak usage times, seasonal trends, or the effect of marketing campaigns. Common techniques include calculating averages, visualizing data with line graphs, and identifying seasonal patterns through decomposition.
On the other hand, predictive time series analysis aims to forecast future values based on historical data. It addresses questions such as "What is likely to happen next?" or "How can we prepare for future events?" Using the same website traffic example, predictive analysis might employ models like ARIMA (AutoRegressive Integrated Moving Average) or machine learning algorithms to anticipate future visitor numbers. These models take into account historical trends and patterns spotted in descriptive analysis to make educated forecasts.
In summary, while descriptive time series analysis provides valuable insights into past behavior and established trends, predictive analysis leverages that information to project future outcomes. For developers, understanding the distinction between these two approaches is crucial for effectively analyzing data and making informed decisions based on both historical insights and future predictions.