Build transparent baselines
Implement three forecasts whose inputs and assumptions can be inspected.
Python and data workflows · EDITION 1.0.0
Build Forecast Baselines You Can Evaluate
Implement last-value, seasonal-naive and moving-mean forecasts on a daily series, then compare them using the same one-step rolling-origin evaluation.
By Sanjaay Gowda T S · For analysts who want a trustworthy baseline before trying more complex forecasting models.
You need: Basic Python lists and functions; Arithmetic mean and absolute difference; Calendar dates and ordered observations
Runtime and hardware details
Python 3.10 or later and a terminal. The supplied project runs locally and uses the Python standard library.
No model, API key, cloud account or network calls are used by the companion.
Basic Python lists and functions; Arithmetic mean and absolute difference; Calendar dates and ordered observations.
Full setup and scope ↓$2.99USD · one-time purchase
Prepared with AI assistance. Independent human technical, editorial and assistive-technology reader review remains pending. Automated checks and agent review are not certification.
PDF + EPUB + companion project. Any applicable tax is shown at checkout. Single-reader ebook terms; companion code is MIT licensed.
TRY THE FREE SAMPLE
See how the guide teaches.
Read 7 sample pages to explore the introduction, first chapter and teaching approach before you choose the complete edition.
Read free sample PDF · No checkout required
WHAT YOU WILL LEARN
Understand each part.
Inspect every boundary.
Evaluate without leakage
Use only observations available before each forecast origin.
Interpret several errors
Compare MAE, RMSE and bias without claiming guaranteed future performance.
INSIDE THE BOOK
9 focused chapters.
- Define the forecast decision
- Prepare a chronological series
- Build a last-value baseline
- Add a seasonal-naive baseline
- Use a trailing-mean baseline
- Evaluate with rolling origins
- Interpret errors and denominators
- Test leakage and edge cases
- Release a baseline experiment
Your download includes
- 21-page tagged PDF with linked contents
- Reflowable EPUB for ebook readers
- Local Python project, fictional input, recorded expected JSON output and 14 companion tests
- 3 explanatory diagrams
- 9 chapters with exercises and worked answers
Before you begin
Python 3.10 or later and a terminal. The supplied project runs locally and uses the Python standard library.
No model, API key, cloud account or network calls are used by the companion.
Learning prerequisites: Basic Python lists and functions; Arithmetic mean and absolute difference; Calendar dates and ordered observations.
Prepared with AI assistance. Independent human technical, editorial and assistive-technology reader review is pending.
The included examples are synthetic teaching material. Model access, software subscriptions, hosting, ongoing updates and consulting are not included.
CLEAR EXPECTATIONS
Before you buy.
What will I build?
Implement last-value, seasonal-naive and moving-mean forecasts on a daily series, then compare them using the same one-step rolling-origin evaluation. The local companion uses fictional teaching fixtures. Project limits: One complete daily series and one-day horizon; Fixed parameters, no tuning or uncertainty intervals; Synthetic fixture is deliberately periodic; No causal demand recovery or production accuracy claim; Assumes final observations available before each next-day origin.
What was tested?
The 14 companion tests passed on the packaged project. The supplied fictional fixture produced its recorded expected JSON output. EPUBCheck reported no errors or warnings; DAISY Ace and PDF/UA automated checks passed. These local teaching checks are not production accuracy claims or certification. Independent human technical, editorial and assistive-technology reader review remains pending.
How do I get the files?
Complete the secure Lemon Squeezy checkout to access the PDF, EPUB and companion ZIP. Keep your receipt and download access private. This is a one-time purchase of edition 1.0.0; ongoing updates and consulting are not included.
Can I share the book or the companion code?
The full ebook is licensed to one reader. The labelled free sample may be shared unchanged. The companion project is MIT licensed and may be adapted or shared with its licence notice. See the exact ebook terms.
Compare simple demand forecasts with rolling origins, visible errors and no future leakage
One book. A complete learning project. Evidence you can inspect.
AI-assisted · Independent human review pending
$2.99USDBuy ebook