A deep factor model for crop yield forecasting and insurance ratemaking
Effective agricultural insurance and risk management programs rely on accurate crop yield forecasting. In this article, a novel deep factor model for crop yield forecasting and crop insurance ratemaking is proposed. This framework first utilizes a deep autoencoder to extract a latent factor, called...
Main Author: | Zhu, Wenjun |
---|---|
Other Authors: | Nanyang Business School |
Format: | Journal Article |
Language: | English |
Published: |
2023
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/170183 |
Similar Items
-
Flexible weather index insurance design with penalized splines
by: Tan, Ken Seng, et al.
Published: (2023) -
Improved index insurance design and yield estimation using a dynamic factor forecasting approach
by: Li, Hong, et al.
Published: (2022) -
Ratemaking Model of Usage Based Insurance Based on Driving Behaviors Classification
by: Zhishuo Liu, et al.
Published: (2022-06-01) -
Machine Learning in Ratemaking, an Application in Commercial Auto Insurance
by: Spencer Matthews, et al.
Published: (2022-04-01) -
Crop Insurance Policies for Citrus Growers
by: Ariel Singerman
Published: (2018-11-01)