Time Series Analysis

Also known as: Time Series, Temporal Analysis, Forecasting, Trend Analysis

Statistical technique for analyzing chronologically ordered data, identifying trends, seasonality, and making forecasts.

Time Series Analysis is a set of statistical techniques for analyzing data collected at successive time points—monthly sales, brand tracker metrics by wave, weekly search volumes—with the goal of describing historical behavior, identifying patterns (trends, seasonality, cycles), and making forecasts.

Main methods: Time series decomposition (separating trend, seasonality, and residual), ARIMA (AutoRegressive Integrated Moving Average), Prophet (Meta's model especially useful for data with strong seasonality and missing data), and Deep Learning models like LSTM for series with complex patterns.

In market research, time series analysis is fundamental for: sales forecasting, analysis of marketing campaign effectiveness over time, and modeling the impact of external events (pandemics, economic crises) on brand indicators.