Thinking of time series data as a rolling movie of a single variable, showing how it changes over time. Whether collected on a regular basis (such as hourly temperatures) or in real-time (like heart rate), this type of data is incredibly versatile and has many practical applications. These include predicting future trends – such as sales, weather, or stock prices – by analyzing past data. It can also help identify long-term patterns in complex phenomena like climate change, economic growth, and website traffic. Additionally, time series data can be used to spot anomalies, such as fraud or equipment failure. By applying various analysis techniques unlock the secrets hidden within this data. Decomposition separates trends, seasonality, and noise components. Autocorrelation and partial autocorrelation measure relationships between data points at different time lags. The Box-Cox transformation aids analysis by improving data stationarity. Powerful statistical models, like ARIMA/SARIMA, help forecast future trends and understand the data’s behavior.
Autocorrelation (ACF) and partial autocorrelation (PACF) plots are like “fingerprint” tools for time series data. They reveal patterns and dependencies between data points at different time lags. ACF shows the overall correlation, while PACF focuses on the direct relationship after accounting for previous lags. This helps identify the order of the ARIMA model, a powerful tool for capturing trends and seasonality in data. By analyzing ACF and PACF, we can build accurate SARIMAX models to forecast future values of the time series, leading to better decision-making and planning.