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PlanIQ
If you were able to join us for CPX then you would have heard the exciting news that PlanIQ is our latest release - putting the power of intelligent forecasting, with an in-built predictive engine, directly into the hands of the users. Amazon Forecast is the first predictive engine to integrate with PlanIQ and the product…
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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. Outliers can be caused by measurement errors, data entry errors, organic real occurrences such as seasonal effects and other reasons. Since outlier values could impact the accuracy of the predictions produced by forecast models…
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PlanIQ - How to manage NULL values?
In real-world forecasting applications, it is common for datasets to contain null values, which represent missing values for specific points in time. There can be multiple reasons for the presence of missing values. For example, a transaction may not have occurred, or a device or service that monitors data may have…
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PlanIQ - Algorithm selection by item: mix and match your forecast
What is algorithm selection by item? Algorithm selection by item is the use of more than one algorithm to produce optimal forecasts across multiple items. Instead of using forecasts generated by a single algorithm for all items in the historical data module, where the algorithm is selected based on best overall performance…
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PlanIQ - New product introduction: all you ever wondered about starting your forecast from scratch
New Product Introduction (NPI) or Cold Start in time series forecasting is a scenario where forecasts should be produced for new items (e.g. new products, new SKUs) for which there is limited historical data, or no historical data at all. Items with no historical data In scenarios where the new items have no historical…
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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 data. Examples for attributes could be style, category, geographic location, size, item hierarchy level etc. The advanced neural network machine learning algorithms supported by PlanIQ can…
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OEG Best Practice: PlanIQ: Design and build your item list for forecasting
* What is the best practice for concatenation in Anaplan? * Step-by-step overview * Level 1 – code inefficient * Level 2 – code better * Level 3 – Code to use * Level 4 – Optimized code to use * How do we handle items with limited to no history? * Adding “Cold Start” Items Before diving in, if you need to refresh your…
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PlanIQ for Predictive Forecasting and Agile Scenario Planning
Anaplan Extends Intelligence Capabilities with PlanIQ for Predictive Forecasting and Agile Scenario Planning September 15, 2020 New integration with Amazon Forecast arms business users with easy to understand machine learning-driven insights and predictions SAN FRANCISCO, CA, SEPTEMBER 15, 2020 — Anaplan, Inc. (NYSE:…
