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Challenge

Qatar Construction Material Cost Forecasting Engine

Qatar's construction surge (6.7% growth) driven by strategic mega-investments requires precise forecasting of rebar, cement, and aggregate price trends. Build a time-series forecasting model using Prophet and Pandas to predict local material cost fluctuations 6 months ahead. Maintain a Mean Absolute Percentage Error (MAPE) below 5%.

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Challenge brief

What you are building

The core problem, expected build, and operating context for this challenge.

Develop a material price forecasting pipeline using Prophet and Python for Qatar's construction sector.

Delivery guide

How work is evaluated

Evaluation

Evaluates MAPE performance of material cost predictions across a 6-month test horizon.

Datasets

Challenge datasets

Public datasets, plus the private datasets you have access to.

Evaluation rubric

How submissions are scored

These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.

Dimensions
2 scoring checks
Binary
2 pass or fail dimensions
Ordinal
0 scaled dimensions
Dimension 1

MAPE Target Check

Ensures mape_pct is less than 5.0%.

Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Dimension 2

Mean Absolute Percentage Error

Forecast accuracy against hold-out actual prices. • target: 3.5 • range: 0-100

Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Learning goals

What you should walk away with

  • Process historical commodity price data using Pandas

  • Train Meta Prophet model with Qatar construction seasonality patterns

  • Incorporate macroeconomic indicators like shipping index and fuel surcharges

  • Output future price curves with confidence intervals

Resources and assets

Reference links and supporting material

Dataset notes

Qatar construction commodity index history (rebar, cement, concrete) from 2020 to 2026.

Challenge setup

Evaluates MAPE performance of material cost predictions across a 6-month test horizon.

Challenge input

JSON array of objects containing date (ds) and price_usd (y)

Prophet

Standard open-source library for business time-series forecasting.

Evaluated output

JSON object with forecasted_price_6m, mape_pct

Evaluation checks
  • Ensures mape_pct is less than 5.0%.
  • Forecast accuracy against hold-out actual prices. • target: 3.5 • range: 0-100
Configured evaluation evidence
  • Benchmark: TimeSFBench
  • Mean Absolute Percentage Error target: 3.5
  • 1 public reference case
Configured runtime
  • Python execution harness
  • Python sandbox (unavailable on Versalist)

Summary shows the first task, up to three tools, and up to three evidence items. Open the technical recipe to view all configured items.

View technical recipe

Configured tools

Action Space
  • Prophet · Required
  • AutoML (H2O) · Optional
  • Retell AI · Optional

Evaluation contract

  • MAPE Target Check · Weight 1
  • Mean Absolute Percentage Error · Weight 1

Metrics

  • Mean Absolute Percentage Error · Target 3.5

Benchmarks

  • TimeSFBench
  • general

Reference cases

  • 1 public reference case

Workflow tasks

  1. Task 1

    Input: JSON array of objects containing date (ds) and price_usd (y)

    Output: JSON object with forecasted_price_6m, mape_pct

    Execution harness: Python

Evaluation tools

  • Prophet · Time Series Engine

Recipe freeze marker

No recipe freeze time is recorded.

Frequently Asked Questions about Qatar Construction Material Cost Forecasting Engine