Data Science in da Hood

Analyzin' two time series, we gotta find Which one be followin' di other in time Identify di leadin', laggin' flow Many methods, we got to know Cross-Correlation Function Di Cross-Correlation Function, it measures di scene Between two time series, it's di mean As a function of di lag, one relative to di other Help identify lead-lag, one behind di other Steps: Compute di cross-correlation, dat's di vibe Between di two time series, side by side Identify di lag, cross-correlation maximized Positive lag means first series leads, second series lags behind Granger Causality Test Granger causality test, it’s a quest Determine if one series can predict di rest Not imply true causality, but helps di relations Identify lead-lag, between di vibrations Steps: Formulate hypotheses, null an' alternative Null: 𝑋 X don't Granger-cause 𝑌 Y, that's declarative Alternative: 𝑋 X Granger-cause 𝑌 Y, it’s decisive Fit a VAR model, perform di test, be precise Dynamic Time Warping (DTW) Dynamic Time Warping, algorithm it be Measures similarity, in time series, you see May vary in speed, align in non-linear fashion Useful to identify, which series follows which, no distraction Steps: Apply DTW algorithm, to di time series Analyze di warping path, understand di theories Di alignment, lead-lag patterns clear Which series follows which, no need to fear Lead-Lag Regression Lead-lag regression, a method to mention Fit a model, regression, pay attention One time series regressed, on lagged values of di other Analyze coefficients, di lead-lag discover Steps: Create lagged versions, of di second series, man Fit regression model, first series as di main plan Dependent variable, second series lagged be Analyze coefficients, significance, di lead-lag we see Outro Analyzin' time series, we find di flow Lead-lag relations, now we know Methods and steps, laid out in rhyme Power of analysis, standin' test of time