A data scientist is performing exploratory data analysis (EDA) on a dataset from a fleet of industrial turbines to identify precursors to component failure. The dataset contains high-frequency time-series sensor readings (e.g., vibration, temperature) and discrete event logs (e.g., error codes). Standard univariate analysis of individual sensors has not revealed any clear predictive patterns. The primary goal is to identify a specific, complex behavior, defined by a sequence of events across multiple attributes, that reliably signals an impending failure. Which EDA process is most effective for identifying and defining this multi-attribute sequential behavior?
Creating composite features that represent system states based on the sequence of sensor readings and event logs.
Using a scatter plot matrix to visualize pairwise correlations between the initial sensor readings.
Applying time-series decomposition (trend, seasonality, residual) to each individual sensor feed.
Performing a Principal Component Analysis (PCA) on the sensor data to identify the dimensions with the highest variance.
The correct answer involves creating new, composite features that represent the state of the system. This process is a form of feature engineering specifically aimed at capturing complex, sequential behaviors that are not apparent from individual variables. By defining states based on patterns across multiple time-series and log data streams (e.g., 'high vibration followed by rising temperature'), the data scientist is directly identifying and creating a new attribute that represents the 'impending failure' behavior of the object (the turbine).
Principal Component Analysis (PCA) is a dimensionality reduction technique. While it can identify key sources of variance in multivariate data, its components are linear combinations of the original features and are often difficult to interpret as a specific, concrete sequence of events. It is not designed to explicitly discover sequential patterns.
Time-series decomposition is typically a univariate method used to separate a single time series into trend, seasonal, and residual components. The scenario explicitly states that univariate analysis is insufficient and the pattern exists across multiple attributes.
A scatter plot matrix is used to visualize pairwise relationships between variables. It is not effective for identifying sequential patterns that unfold over time and involve interactions between more than two variables.
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