This website uses cookies.
We use cookies to improve your online experience. By continuing to use our website we assume you agree to the placement of these cookies.
To learn more, you can find in our
Privacy Policy.
Accept
Gain insight into
research trends
and
influential papers
with
Research Intelligence.
Search everything
Product tour
About us
Our data
Use cases
Pricing
Sign in
Sign up
Home
Research Intelligence
Pro
Expert Finder
Pro
Scinapse Trends
Paper Search
Journal Search
Collections
Favorites
History
Notes
Keyword Alerts
Submit Feedback
Settings
doi.org/10.1016/j.cie.2020.106435
Other
An optimized model using LSTM network for demand forecasting
Hossein Abbasimehr
15
,
Mostafa Shabani
7
,
Mohsen Yousefi
4
View all 3 authors
Computers & Industrial Engineering
6.70
Volume: 143, Pages: 106435 - 106435
Published
: Mar 30, 2020
369
Citations
Source
Cite
Save
Basic Info
Analytics
References
Citations
Abstract
No abstract.
Paper Fields
Hyperparameter optimization
Support vector machine
Time series
Demand forecasting
Operations research
Data mining
Exponential smoothing
Computer science
Paleontology
Computer vision
Artificial neural network
Hyperparameter
Engineering
Series (stratigraphy)
Artificial intelligence
Mathematics
Biology
Autoregressive model
Autoregressive integrated moving average
Machine learning
Econometrics
Smoothing
Paper Details
Title
An optimized model using LSTM network for demand forecasting
DOI
doi.org/10.1016/j.cie.2020.106435
Published Date
Mar 30, 2020
Journal
Computers & Industrial Engineering
Volume
143
Pages
106435 - 106435
Notes
To use the Note feature, you need to be logged in. Please
sign in.
History
View all history
Assessment of reconfiguration schemes for Reconfigurable Manufacturing Systems based on resources and lead time
Step 1. Scroll down for details & analytics related to the paper.
Discover a range of citation analytics, paper references, a list of cited papers, and more.
Skip
Next