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Forecaster Explainability Results Clarification
We transitioned from PlanIQ to Forecaster and started importing explainability results into the model. But I’m struggling to interpret some of the since there is no information in Anapedia. Here are examples from the sales explainability results list: Historical: trend (ETS, ENSEMBLE) Historical: linear_trend (ENSEMBLE,…
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Recording now available! January 29 platform release event
If you missed our January 29, 2026 platform release event, the recording is now available! In this webinar, we highlighted the most recent feature releases and how to take advantage of these in your environment. Our expert-led session demonstrated new features and innovations, provided a technical deep-dive and “how-to”,…
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Forecaster and related data
Background Customers are frequently wondering what can be used as related data. When thinking about related data, we can think about different types of “internal” time dependent inputs: Historical and future promotions Stockouts Price fluctuations Holidays and special events Actual data may be different dependent on a…
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How can i export accuracy metrics from Anaplan Forecaster to Model.
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Register for our January 29 platform release webinar!
Anaplan's next quarterly platform release webinar will be held January 29, 2026 at 8 a.m. Pacific Time. During this session, we will explore the latest features so you can optimize your Anaplan experience. Our expert-led session will equip you with a practical guide and live demonstrations to help you get the most out of…
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Forecaster - Using a backtest window to assess performance
In almost all cases, computing forecast accuracy requires a calculation of the difference between forecast versus actuals. One of the best ways to approximate the future accuracy of the forecast is to use a backtest window. With a backtest window, you can simulate “what if” you had run a Forecaster forecast with the data…
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November 2025 platform releases and what’s next
Check out the latest Anaplan feature updates and enhancements in our November 2025 official release notes. The information below offers supplemental information to that post. Intelligence Anaplan Forecaster: General enhancements Anaplan Forecaster now supports list subsets for the item ID list and custom time, list format…
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Forecaster - How to manage NULL values?
In real-world forecasting applications, it's common for data to have null values. These values are missing values for certain 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 malfunctioned.…
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[Start Here] Forecaster overview and resources
What is Forecaster? Who is it for? Use-case examples To get started Once you get more comfortable Deep dive on algorithms Support Are you interested in starting time series forecasting with Forecaster on Anaplan, but don’t know where to start? You have come to the right place! This article is the pathway to all things…
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Forecaster – Considerations before starting
Before starting with Forecaster, there are a few topics we recommend considering around the data, properties of the forecast, and forecast evaluation. While it is important to define clear goals for each forecasting scenario, Forecaster's forecast accuracy will mostly be determined by the quality of the available data.…
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Forecaster algorithm properties
Review the table below for information on each algorithm and its specific properties.
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Forecaster - Probabilistic forecasting using forecast quantiles
Probabilistic forecasts & forecast quantiles Forecaster produces probabilistic forecasts. This means that Forecaster algorithms output a distribution of possible values, rather than a single point forecast typically outputted by other solutions. This distribution can be divided into quantiles. Applied to forecasting,…
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Forecaster - Introduction to forecast evaluation
Before making business decisions based on Forecaster forecast results, you should assess the expected performance of the forecast predictions in the real world. It’s important to evaluate forecast performance at the outset of your project as well as on an ongoing basis. Also consider that a range of factors unique to your…
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Forecaster - Deep dive into the Algorithms
Overview In this article, we review the algorithms that drive time series forecasting with Forecaster. These algorithms range from traditional statistical algorithms such as Exponential Smoothing (ETS), to those based on complex neural network algorithms such as DeepAR. Before talking about the algorithms in more detail,…
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Forecaster - How to use item attributes to refine your forecast
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 DeepAR algorithm can leverage the information captured in these attributes to produce…
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Forecaster - Design and build your item list for forecasting
In order to forecast forward based on historical data, the algorithms within Forecaster require a module in Anaplan made up of a minimum of the following: a list of items to forecast against (e.g., SKU by Store), a list of time periods containing the history the data or measure to be forecasted (e.g. sales volumes) This…
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PlanIQ algorithm properties
Review the table below for information on each algorithm and its specific properties. Question? Leave a comment!
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[Start Here] PlanIQ overview and resources
* First things first * What is PlanIQ? * Who is it for? * Use case examples * Now, let's go under the hood * To get started * Once you get more comfortable * Deep dive on algorithms * Support You are interested in starting time series forecasting with PlanIQ on Anaplan, but you don’t know where to start? You have come to…
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PlanIQ and related data
Author: Evgy Kontorovich, Sr. Director Product Management at Anaplan. Background Customers are frequently wondering what can be used as related data. When thinking about related data, we can think about different types of “internal” time dependent inputs: Historical and future promotions Stockouts Price fluctuations…
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PlanIQ - Probabilistic forecasting using forecast quantiles
Probabilistic forecasts & forecast quantiles PlanIQ produces probabilistic forecasts. This means that PlanIQ algorithms output a distribution of possible values, rather than a single point forecast typically outputted by other solutions. This distribution can be divided into quantiles. Applied to forecasting, quantiles…
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PlanIQ - Deep dive on the algorithms under the hood
Baseline time series algorithms Advanced statistical time series algorithms Flexible local algorithms Neural network algorithms Summary Companies often develop plans and business strategies to drive decisions and actions around finance, operations, supply chain and other areas. They do this based on how they perceive the…
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PlanIQ – Considerations Before Starting to Forecast
Before starting to forecast with PlanIQ, there are a few topics we recommend considering around the data, properties of the forecast, and forecast evaluation. While it is important to define clear goals for each forecasting scenario, PlanIQ's forecast accuracy will mostly be determined by the quality of the available data.…
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PlanIQ: Using a backtest window to assess performance
In almost all cases, computing forecast accuracy requires a calculation of the difference between forecast versus actuals. One of the best ways to approximate the future accuracy of the forecast is to use a backtest window. With a backtest window, you can simulate “what if” you had run a PlanIQ forecast with the data…
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PlanIQ: Introduction to forecast evaluation
Before making business decisions based on PlanIQ forecast results, you should assess the expected performance of the forecast predictions in the real world. It’s important to evaluate forecast performance at the outset of your project as well as on an ongoing basis. Also consider that a range of factors unique to your…
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PlanIQ for forecasting
PlanIQ provides us with an easy-to-use and intelligent time series forecasting tool. It integrates with the Anaplan platform and incorporates advanced machine learning and statistical forecasting algorithms. In this article, I explore functionalities that PlanIQ offers using a sample data set and the preparation steps…
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Start Here - PlanIQ Overview and Resources
Start here: PlanIQ overview and resources Are you are interested in starting time series forecasting with PlanIQ on Anaplan, but you don’t know where to start? You have come to the right place! Here are two resources to get you started: Start your journey with Anaplan PlanIQ! What is PlanIQ, who is it for, use case…
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PlanIQ Best Practices
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! PlanIQ - Deep dive on the Algorithms under the hood Learn more about Baseline time series algorithms, Flexible local…
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Forecaster: 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…