Time series analysis

exploratory statistics

slug: recipe-exploratory-statistics-time-series-analysis

Recipe: Time series analysis

category: exploratory statistics

Problem

You need to analyze trends or patterns over time:

• detect seasonality or cyclic behavior
• identify spikes, dips, or anomalies
• summarize time-based metrics for reporting or forecasting

Solution

Follow these steps to perform time series analysis:

• load the dataset
• ensure the date/time field is properly formatted
• aggregate numeric metrics by time intervals (daily, weekly, monthly)
• apply [[step-timeSeries]] to compute trends, moving averages, or rolling statistics
• optionally visualize time series data

Step Sequence

load step -> [[step-timeSeries]] -> calculate step -> chart step

Input Datasets

• transactions_clean — cleaned transactional data with date/time fields
• Notes: include fields like transaction_date and numeric measures such as amount

Output Dataset

• time_series_summary — dataset aggregated by time intervals with computed metrics
• Notes: ready for trend analysis, anomaly detection, or reporting

Step-By-Step Explanation

Step	Purpose	Notes
load step	Load dataset	Supports local file, database, or API sources
[[step-timeSeries]]	Compute time-based metrics	Example: daily totals, moving averages, rolling sums
calculate step	Derive additional fields	Example: percent change, cumulative totals
chart step	Visualize trends over time	Optional line chart, area chart, or bar chart

Variations & Extensions

• Aggregate by different intervals (hourly, weekly, quarterly)
• Combine with filter step to focus on specific categories or segments
• Apply [step-corr] or [step-rank] to explore relationships with other variables over time

Concepts Demonstrated

• Time-based aggregation and analysis
• Trend and seasonality detection
• Rolling and cumulative calculations
• Sequencing analytics and visualization steps

Related Recipes

• Univariate analysis of numeric variables
• Frequency analysis of categorical data

Notes & Best Practices

• Ensure date/time fields are standardized and correctly typed
• Handle missing or irregular time intervals carefully
• Visualize early to detect patterns or anomalies

Metadata

title: "Time series analysis"
category: "exploratory statistics"
difficulty: "Intermediate"
tags: [time-series, trends, rolling, EDA]
inputs: [transactions_clean]
outputs: [time_series_summary]
steps: [step-load, step-timeSeries, step-calculate, step-chart]
author: "Tom Argiro"
last_updated: "2025-10-25"
doc_type: "recipe"