Time series analysis

Q: How deeply does Starting Data Analytics with Generative AI and Python cover time series analysis?

Demonstrates plotting and daily or monthly aggregation, but stops short of detailed predictive modeling.

As explained in Starting Data Analytics with Generative AI and Python

Time series analysis is the analysis of observations ordered in time to identify trends, cycles, dependencies, and other temporal behavior.

Time series analysis examines observations ordered by time to reveal trends, cycles, dependencies, and other temporal behavior. Aggregating data at a suitable frequency can make recurring patterns easier to inspect. section 2.6.2

A practical workflow includes:

Inspect apparent signals cautiously; weak cycles may not justify deeper predictive analysis. section 2.6.2

Review scores over time

Monthly aggregation revealed a spike in low review scores during November and December 2016. A possible spring cycle appeared, but the signal was too weak to investigate further without a precise predictive goal. section 2.6.2

How Starting Data Analytics with Generative AI and Python covers it

Cover of Starting Data Analytics with Generative AI and Python

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Also explained in

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The Art of AI Product DevelopmentTime series analysis is the study of observations ordered over time to identify trends, seasonal patterns, anomalies, and other temporal behavior.
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Outlier Detection in PythonTime-series analysis is the analysis of observations ordered over time to characterize trends, cycles, dependencies, or unusual temporal behavior.
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Statistics Every Programmer NeedsTime series analysis is the analysis of observations ordered over time to identify temporal patterns, dependence, or trends.

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Related concepts

sequence miningcontrasts with · Sequence mining discovers recurring ordered relationships or patterns in sequences of events or item sets.
datasetuses · A dataset is an organized collection of related data items, records, or observations treated as a unit for storage, processing, analysis, or computation.
anomaly detectionuses · Anomaly detection is the identification of observations, events, or behaviors that substantially deviate from expected patterns or a normal baseline.
granularityconfigured by · Granularity is the level of detail or fineness at which information, behavior, or a system is represented, grouped, or divided.
differencingused by · Differencing is the operation of subtracting an earlier observation from a later observation in a time series to reduce temporal trends or seasonal effects.

This explainer was generated by Manning's knowledge graph from the text of Starting Data Analytics with Generative AI and Python. The section links point to the parts of the book it draws on.