Analyzing time series data

Autocorrelation and partial autocorrelation are crucial tools in time series analysis that provide insights into data patterns and guide model selection. For outlier detection, they help distinguish between genuine anomalies and expected variations, leading to more accurate and context-aware outlier identification.

Autocorrelation and partial autocorrelation

Autocorrelation refers to correlating a time series with its own lagged values. Simply put, it measures how each observation in a time series is related to its past observations. Autocorrelation is a crucial concept in understanding the temporal dependencies and patterns present in time series data.

Partial autocorrelation function (PACF), on the other hand, is a statistical tool that’s used in time series analysis to measure the correlation between a time series and its lagged values after removing the effects of intermediate lags. It provides a more direct measure of the relationship...