📄️ SensibleAI Forecast: Data Validation & Exploration
Prior to beginning Rapid Project Experimentation (RPE), a SensibleAI Forecast implementor must verify that the data quality is sufficient by ensuring the below criteria are met. This can be accomplished through SQL manipulations using Query Composer within AI Data Manipulator (DMA).
📄️ Ensuring Quality Time Series Data for ML
In the realm of time series data, the foundation for effective time series machine learning lies in the quality of the datasets used.
📄️ Data Quality Enhancement Best Practices
This article will walk through many of the key principles associated with taking a data set and making it ready for SensibleAI Forecast.
📄️ Initial Data Validation - Dataset Page Insights
During the beginning of an implementation, one of the most important steps is to understand the dataset to be worked on. While first steps may include understanding target hierarchies and conversions, or data sparsity and target counts, further analysis can be done during the first project builds in SensibleAI Forecast.
📄️ Target Depth & Granularity Considerations
Customers often ask critical questions about SensibleAI Forecast related to choosing the optimal target depth, time granularity, and whether to forecast at the unit or dollar level.
📄️ Overcoming Data Collection Lag
Collection Lag is defined as the time duration between this given moment in time and when you receive the corresponding data for that moment in time.