Missing values in time series are not straightforward to deal with. In my book on time series forecasting, Modern Time Series Forecasting with Python, I talk about a few techniques, right from thinking about the missing values in the right way to some algorithmic techniques to deal with missing data (like seasonal interpolation). Originally, that … Continue reading Time Series Imputation using Matrix Recovery
Intermittent Demand Forecasting with Deep Renewal Processes
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
Forecast Error Measures: Intermittent Demand
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. Source Casually, we call intermittent series as series … Continue reading Forecast Error Measures: Intermittent Demand
Forecast Error Measures: Scaled, Relative, and other Errors
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. Taxonomy of Forecast Error Measures Both Scaled Error and Relative Error are extrinsic error measures. They depend on another reference forecast to evaluate … Continue reading Forecast Error Measures: Scaled, Relative, and other Errors
Forecast Error Measures: Understanding them through experiments
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 should … Continue reading Forecast Error Measures: Understanding them through experiments
Deep & Shallow 