Datathon Kaufland Solution – LSTM and EDM Models for Predictive Maintenance

In this paper we propose the use of a combination of LSTM and EDM models to address the issue of anomaly classification and prediction in time series data. Working with sensor data for automated storage and retrieval systems for a German hypermarket chain, we show that predictors based on variance and median methods show sufficient promise in the handling of anomalies.

Datathon – Sofia Air 2.0 – Solution – Wonder Gang: Articles on Particles

Techonnology and methods used: R – tidyverse, lubridate, snakecase, sp, raster, spData, sf, leaflet, mapview, ggplot2, shiny, maps, devtools, geojsonio, rgdal, leaflet.esri, leaflet.extras Python – s3fs, pandas, numpy, matplotlib, plotly 1. Business Understanding Air pollution beyond the norms is a […]