PlanIQ Best Practices

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PlanIQ Best Practices

In the articles below, you will find many of PlanIQ's best practices.

Learn how to deal with outliers, how to manage NULL values, how to use forecasting quantiles and many more!

 

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PlanIQ - Deep dive on the Algorithms under the hood

Learn more about Baseline time series algorithms, Flexible local algorithms and Neural network algorithms.

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PlanIQ - Dealing with outliers

Outliers in time series data are values that differ greatly from the rest of the time series. Learn how to handle them as part of PlanIQ!

 

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PlanIQ - How to use item attributes to refine your forecast

Metadata attributes are static, non-time dependent categorical text features that describe the items in the historical time series.

Learn how to use this data to improve your forecast!

 

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PlanIQ - How to manage NULL values

Null represent missing values for specific points in time.
Learn how to manage them within your data set.

 

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PlanIQ - Probabilisitc forecasting using forecast quantiles

PlanIQ algorithms produce probabilisitic forecast quantiles, which refer to a distribution of possible values. 

Learn more about those values and how to use them wisely based on your use case!

 

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PlanIQ - Algorithm selection by item: Mix and Match your forecast!

What is algorithm selection by item? 

Learn more about "model blending" - the use of more than one algorithm to produce optimal forecasts across multiple items.

 

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PlanIQ - New Product Introduction

All you ever wondered about starting your forecast from scratch!

 

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PlanIQ - Design and build your item list for forecasting

What is the best practice for concatenation in Anaplan? 

A step-by-step overview for creating a concatenated item list.

 

 

 

 

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