Who should use the Time-Series Data Analysis and Reporting workflow?
Teams or solo builders working on business tasks who want a repeatable process instead of one-off tool experiments.
Execution Map
3-step pipeline — follow each step in order
1Setup · Step 1 of 3
Open task pagePrepare Time-Series Data
Use natural language queries to retrieve and prepare time-series data from databases or CSV files, ensuring the data is clean and formatted for analysis.
Why this matters
This step sets up the raw data foundation, without which the analysis would be based on incomplete or unstructured inputs.
What you get
A clean, structured time-series dataset is ready for in-depth analysis in the next step.
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2Input · Step 2 of 3
Open task pageAnalyze Time-Series Data
Analyze time-series data using Orange Data Mining to identify trends, seasonality, and anomalies, generating a comprehensive statistical summary and visualizations.
Why this matters
This is the core analytical step where time-series patterns are extracted and quantified, directly producing the key insights.
What you get
Trends, seasonal patterns, and anomalies are identified and documented, forming the basis for decision-making.
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3Deliver · Step 3 of 3
Open task pageGenerate Business Reports
Generate business reports with HubSpot ChatSpot to compile the analysis results into a formatted PDF or dashboard, ready for stakeholder presentation.
Why this matters
This step transforms analytical output into a shareable, professional report that can be delivered to business users or clients.
What you get
A polished report containing charts, summary statistics, and recommendations is generated and ready for distribution.
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