Time-Series Analysis — Complete Guide
Time-Series Analysis — Complete Guide: free step-by-step lesson with examples, common mistakes, and interview tips — part of ML.NET Tutorial on Toolliyo Academy.
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ML.NET Tutorial · Lesson 49 of 100
Time-Series Analysis
Foundations ✓ → Models → NLP & advanced → MLOps
Models · 2 — Classify & regress · ~6 min · Module 5: Regression Models
What is this?
Time-series analysis studies trend, seasonality, and residuals before or with forecasting.
Why should you care?
AIPredict analysts plot Demand before trusting SSA output.
See it live — copy this example
Use a .NET console or Web API project with Microsoft.ML. Run dotnet run after pasting.
// explore then model
var rows = ml.Data.CreateEnumerable<DayDemand>(data, reuseRowObject: false).ToList();
Console.WriteLine($"n={rows.Count} mean={rows.Average(r => r.Demand):F1}");What happened?
- Plot ACF mentally: weekly seasonality → windowSize 7.
- Stationarity helps.
- Outliers distort SSA.
Practice next
- Compute mean/std by weekday.
- Note seasonality.
- Choose windowSize.
- Remove spike day and refit.
- Compare weekday means.
Remember
Explore first. Seasonality → windows. Handle spikes.
AIPredict series EDA
Demand shows weekly seasonality.
Outcome: SSA windowSize=7 justified.
Interview prep for this lesson
Practice these questions aloud after reading—each links to a full structured answer.
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