Time-Series Forecasting — Complete Guide
Time-Series Forecasting — 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 16 of 100
Time-Series Forecasting
Foundations → Models → NLP & advanced → MLOps
Foundations · 1 — Context & data · ~6 min · Module 2: Machine Learning Basics
What is this?
Time-series forecasting predicts future values from ordered history (SSA and related trainers).
Why should you care?
AIPredict inventory and revenue jobs need next-week demand, not shuffled rows.
See it live — copy this example
Use a .NET console or Web API project with Microsoft.ML. Run dotnet run after pasting.
var pipeline = ml.Forecasting.ForecastBySsa(
outputColumnName: "Forecast",
inputColumnName: "Demand",
windowSize: 7,
seriesLength: 30,
trainSize: 90,
horizon: 7);What happened?
- Respect time order — no random shuffle.
- Horizon is how many steps ahead.
- Retrain as new days arrive.
Practice next
- Prepare daily Demand series.
- ForecastBySsa horizon 7.
- Plot forecast vs actual.
- Horizon 14.
- Add holiday dummy outside SSA.
Remember
Ordered series. SSA forecast. Retrain on new days.
AIPredict 7-day demand
Warehouse plans next week stock.
Outcome: Buyers see a forecast curve.
Interview prep for this lesson
Practice these questions aloud after reading—each links to a full structured answer.
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