Let’s face it, anyone who has worked on Time Series Forecasting problems in the retail, logistics, e-commerce etc. would have definitely cursed that long tail which never behaves. The dreaded intermittent time series which makes the job of a forecaster difficult. This nuisance renders most of the standard forecasting techniques impractical, raises questions about the … Continue reading Intermittent Demand Forecasting with Deep Renewal Processes
In the previous few blog posts, we’ve seen all the popular forecast measures used in practice. But all of them were really focused on smooth and steady time series. But there is a whole different breed of time series in real life – intermittent and lumpy demand. Casually, we call intermittent series as series with … Continue reading Forecast Error Measures: Intermittent Demand
Following through from my previous blog about the standard Absolute, Squared and Percent Errors, let’s take a look at the alternatives – Scaled, Relative and other Error measures for Time Series Forecasting. Both Scaled Error and Relative Error are extrinsic error measures. They depend on another reference forecast to evaluate itself, and more often than … Continue reading Forecast Error Measures: Scaled, Relative, and other Errors
Measurement is the first step that leads to control and eventually improvement. H. James Harrington In many business applications, the ability to plan ahead is paramount and in a majority of such scenario we use forecasts to help us plan ahead. For eg., If I run a retail store, how many boxes of that shampoo … Continue reading Forecast Error Measures: Understanding them through experiments
We have come a long way in the world of Gradient Boosting. If you have followed the whole series, you should have a much better understanding about the theory and practical aspects of the major algorithms in this space. After a grim walk through the math and theory behind these algorithms, I thought it would … Continue reading The Gradient Boosters VII: Battle of the Boosters
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