"Demand Forecasting Intermittent and Lumpy Time Series: Comparing ..."

Daniel Kiefer et al. (2021)

> Home

Details and statistics

DOI: —

access: open

type: Conference or Workshop Paper

metadata version: 2022-05-04

- view
  - electronic edition via handle.net (open access)
- export record
  dblp key:
  - conf/hicss/KieferGBD21
- ask others
- share record
  persistent URL:
  - https://dblp.org/rec/conf/hicss/KieferGBD21
Daniel Kiefer, Florian Grimm, Markus Bauer, Clemens van Dinther:
Demand Forecasting Intermittent and Lumpy Time Series: Comparing Statistical, Machine Learning and Deep Learning Methods. HICSS 2021: 1-10

manage site settings

To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.