Airline Passenger Data
Trend, evolving seasonality and long-term growth
Since 2014 · Automated statistical modelling
Artificial intelligence for data science.
Automated data analysis, interpretable models and human-readable statistical reports for the modern data stack.
Explore how machines can discover structure in data, compare models, explain patterns and turn complex analysis into understandable reports.
Airline Passenger Analysis
OBSERVED
TREND
FORECAST · 95% CI
Long-term trend
Annual periodic structure
Increasing seasonal amplitude
Residual variation
TREND DETECTED
+2.7% / year
PERIODIC COMPONENT
12 month cycle
CHANGE POINT
Detected: 2008
MODEL CONFIDENCE · HIGH
The method
From raw data to an understandable model
DATAMODEL DISCOVERYEVALUATIONEXPLANATIONREPORT
01
Explore
Inspect structure, missingness, distributions and relationships.
02
Discover
Search across candidate statistical models and structural explanations.
03
Evaluate
Compare fit, complexity, predictive performance and uncertainty.
04
Explain
Turn discovered patterns into charts, predictions and readable descriptions.
Example reports
See what automated analysis can uncover
Solar Irradiance
Long-term structure, variation and periodic behaviour
Unemployment
Structural change, trends and uncertainty over time
Topics
Automated data science has evolved
Automated Statistical Analysis
Discover patterns and choose appropriate analytical methods automatically.
AutoML
Automated model selection, tuning and evaluation.
Explainable AI
Make machine-generated conclusions understandable and auditable.
Gaussian Processes
Probabilistic modelling with interpretable uncertainty.
Time-Series Intelligence
Detect trends, seasonality, change points and anomalies.
AI Analytics
Modern AI systems that help analysts explore and understand data.
Origins
The idea behind the Automatic Statistician
The original Automatic Statistician research investigated whether parts of statistical modelling could be automated without giving up interpretability. Rather than fitting a single fixed model, the system searched a compositional space of Gaussian process kernels, building structure out of smooth trends, periodic components, changepoints and noise.
Candidate models were compared using Bayesian reasoning, which balances fit against complexity. The discovered structure was then translated into natural-language descriptions and charts, together with model criticism that flagged where the model disagreed with the data. The result was a report a statistician could read, check and argue with.
Explore the Research
SE × PER
+ SE × LIN
+ CP(2008) × SE
+ WN
+ SE × LIN
+ CP(2008) × SE
+ WN
SE — smooth trend
PER — periodicity
LIN — linear growth
CP — changepoint
WN — noise
BIC — model score
Guides
Learn automated data analysis
Julius AI Review 2026: Does It Actually Earn Its Place in Data Science Workflows?
Julius AI is a conversational data analysis platform for non-coders and analysts who need rapid EDA. Upload a CSV, ask questions in plain English, get statistical analysis and visualisations. Best for rapid exploratory analysis and communicating findings to non-technical stakeholders. Not a replacement for Jupyter for advanced work.
B.Tech in AI and Machine Learning from BITS Pilani WILP: The 2026 Complete Guide
BITS Pilani WILP B.Tech in AI & ML is a 4-year online degree for working professionals. Cost: ~INR 50–55K/semester (~INR 4–4.4L total). Best for professionals without a CS background who need a recognised undergraduate AI/ML credential for career transitions. Slower and more expensive than bootcamps but provides a degree.
Choropleth Mapping in Python: A Practical Guide to Geographic Data Science
The geometry is the easy part of a choropleth map. Classification is where most maps actually go wrong, here's how to get it right in Python.
Who Owns Perplexity AI? The Complete Business Model and Ownership Structure Explained
Perplexity AI was founded in 2022 by four ML researchers from Google, Meta, and DeepMind. Backed by NVIDIA, Jeff Bezos, IVP, and NEA. Valued at USD 9 billion as of 2025. Business model: freemium (Pro at USD 20/month) + API licensing. Key differentiator from Google: answers not links, no advertising.
Data Science Outsourcing in 2026: Complete Guide to Costs, Risks, Best Practices, and Where to Find Quality Partners
Data science outsourcing saves 30–60% versus US rates and accelerates delivery by months. Best for well-defined ML projects, data engineering, and specialist capability gaps. Requires internal ownership of strategy and data governance. Key risk: IP lock-in — require open-source frameworks and comprehensive documentation.
Machine Learning in Logistics: Real Benefits, Use Cases, and Lessons from the Field
Demand forecasting, route optimization, predictive maintenance: five ways ML is used in logistics, and what actually determines success in practice.
Software
Tools for automated data science
Reviews and comparisons of the platforms that automate parts of the analytical workflow, evaluated against the methodology we publish.
Explore Data Science Tools