Forecasting aluminum prices, leveraging statistical forecasting & leading indicators

In this case study, the spotlight is on forecasting aluminum prices.
We examine the relationship between prevailing market sentiment and the volatility of aluminum prices, and utilize this intel to uncover the most relevant leading indicators.

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What's in this guide

We show you how taking a data-driven approach can help you identify the relevant leading indicators and improve forecast accuracy.

  • Examining the symbiosis between aluminum prices and the industry at large

  • The importance of selecting and testing the right leading indicators

  • Leveraging statistical forecasting to build robust, accurate forecasts


How to mitigate forecast biases and human errors

Many organizations depend on correlation for indicator selection. Find out why this is cause for concern, and we'll show you a better way to select your indicators.

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Are you using these costly and inadequate forecasting methods?

Explore the common mistakes and fallacies in forecasting, and why Excel can only get you this far.

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A data-driven approach to indicator identification

Learn how you can combine the best from econometrics and machine learning literature to improve forecast performance.

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Contact us and we'll show you how you can improve your forecast accuracy by 40-60% today.

Whether your goal is to increase market share or safeguard against volatility,
the road to making decisions confidently lies in generating accurate forecasts you can trust.

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