InTowards AIbyAlexandre WarembourgIntroduction to Nixtla for Demand Forecasting.Through the M5 Demand Forecasting Competition, learn how to use the Nixtla forecasting package.Dec 13, 20231Dec 13, 20231
InTowards AIbyAlexandre WarembourgA Long-Term Demand Forecasting Model Implementation Case Study with a Major RetailerExplore how I developed a core demand forecasting algorithm for ten countries, dealing with an average product sales history of 11 months…Jan 8, 20242Jan 8, 20242
InTDS ArchivebyDavide BurbaForecast Multiple Horizons: an Example with Weather DataPredict precipitations in Switzerland by using the forecasting horizon as a feature.Aug 6, 20231Aug 6, 20231
InTDS ArchivebyNicolas VandeputDo You Read Excel Files with Python? There is a 1000x Faster Way.In this article, I’ll show you five ways to load data in Python. Achieving a speedup of 3 orders of magnitude.Jul 3, 202121Jul 3, 202121
InTDS ArchivebyNicolas VandeputDo You Use Apply in Pandas? There is a 600x Faster WayBy leveraging vectorization and data types, you can massively speed up complex computations in PandasAug 23, 20217Aug 23, 20217
InTDS ArchivebyNicolas VandeputDo You Use XGBoost? There is a 200x Faster WayIn this article, I’ll show you four ways to train XGBoost. We’ll achieve a 200x speed-up compared to XGBoost by-default settings.Jul 29, 2021Jul 29, 2021
InArtefact Engineering and Data SciencebyYoussef OudghiriEncoding categorical features in forecasting: are we all doing it wrong?A novel approach for encoding categorical variables, designed to enhance trend modeling, increase forecasting accuracy, and reduce bias.Jun 28, 20232Jun 28, 20232
InTrusted Data Science @ HaleonbyLeonidas TsaprounisMetrics for Distributional ForecastsHow to evaluate distributional/probabilistic time series forecasts in Python.Feb 27, 20233Feb 27, 20233
InTDS ArchivebyVitor CerqueiraTime Series for Climate Change: Forecasting Energy DemandHow to use time series analysis and forecasting to tackle climate changeMay 2, 20235May 2, 20235
InTDS ArchivebyMarco CerlianiForecast Time Series with Missing Values: Beyond Linear InterpolationComparing Alternatives to Handle Missing Values in Time SeriesOct 13, 2022Oct 13, 2022
InTDS ArchivebyNikos KafritsasTime-Series Forecasting: Deep Learning vs Statistics — Who Wins?A comprehensive guide on the ultimate dilemmaApr 5, 202317Apr 5, 202317
Cuong Duongfacebook/prophet in 2023 and beyondSince Sean Taylor and Ben Letham open-sourced Prophet in 2017, it has remained a popular tool for forecasting time series, especially in…Feb 26, 20232Feb 26, 20232
InTDS ArchivebyTyler BlumeFixing Prophet’s Forecasting IssueStep 1: Constrain the Insane TrendJan 24, 20235Jan 24, 20235
Nicolas VandeputAn End-to-End Supply Chain Optimization Case Study: Part 1 Demand ForecastingThis is the first part of a supply chain optimization project focusing on demand planning. You can read the second part here.Jan 11, 20232Jan 11, 20232
InArtefact Engineering and Data SciencebyMaxime LutelSales forecasting in retail: what we learned from the M5 competitionOur review of recurrent issues encountered in a sales forecasting project, and how we handled them for the M5 competition.Feb 3, 20215Feb 3, 20215
InTDS ArchivebyMarco PeixeiroThe Easiest Way to Forecast Time Series Using N-BEATSFrom theory to practice, learn how N-BEATS works and apply it in a real-life forecasting project using PythonNov 23, 20226Nov 23, 20226
InTDS ArchivebyNikos KafritsasN-BEATS : Time-Series Forecasting with Neural Basis ExpansionA Deep Learning model that provides accuracy and interpretabilityNov 25, 20222Nov 25, 20222
InTDS ArchivebyMarco PeixeiroTheta Model for Time Series ForecastingA hands-on tutorial on how to apply the Theta model for time series forecasting in PythonNov 2, 20223Nov 2, 20223
InTDS ArchivebyRuan van der MerweImplementing Facebook Prophet efficientlyIf you have ever worked with time series predictions, I am quite sure you are well aware of the strains and pains that come with them. One…Nov 14, 201820Nov 14, 201820