Illinois Superconductor Corp.: Forecasting Demand for Superconducting Filters

2017 ◽  
pp. 1-32
Author(s):  
Mohanbir Sawhney ◽  
Lisa Damkroger ◽  
Greg McGuirk ◽  
Julie Milbratz ◽  
John Rountree
Keyword(s):  
Competitive Dynamics ◽  
Base Stations ◽  
Forecasting Model ◽  
Diffusion Modeling ◽  
Model Based ◽  
Customer Preferences ◽  
Start Up ◽  
Diffusion Effects ◽  
Superconducting Filters ◽  
Simple Chain

Illinois Superconductor Corp. a technology start-up, came up with an innovative new superconducting filter for use in cellular base stations. It needed to estimate the demand for its filters. The manager came up with a simple chain-ratio-based forecasting model that, while simple and intuitive, was too simplistic. The company had also commissioned a research firm to develop a model-based forecast. The model-based forecast used diffusion modeling, analogy-based forecasting, and conjoint analysis to create a forecast that incorporated customer preferences, diffusion effects, and competitive dynamics.To use the data to generate a model-based forecast and to reconcile the model-based forecast with the manager's forecast. Requires sophisticated spreadsheet modeling and the application of advanced forecasting techniques.

Logistics demand forecasting model based on improved neural network algorithm

2020 ◽  
pp. 1-11
Author(s):  
Hongjiang Ma ◽  
Xu Luo
Keyword(s):  
Neural Network ◽  
Influencing Factor ◽  
Demand Forecasting ◽  
Forecasting Model ◽  
Logistics System ◽  
Model Based ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Demand Forecasting Model ◽  
Logistics Demand

The irrationality between the procurement and distribution of the logistics system increases unnecessary circulation links and greatly reduces logistics efficiency, which not only causes a waste of transportation resources, but also increases logistics costs. In order to improve the operation efficiency of the logistics system, based on the improved neural network algorithm, this paper combines the logistic regression algorithm to construct a logistics demand forecasting model based on the improved neural network algorithm. Moreover, according to the characteristics of the complexity of the data in the data mining task itself, this article optimizes the ladder network structure, and combines its supervisory decision-making part with the shallow network to make the model more suitable for logistics demand forecasting. In addition, this paper analyzes the performance of the model based on examples and uses the grey relational analysis method to give the degree of correlation between each influencing factor and logistics demand. The research results show that the model constructed in this paper is reasonable and can be analyzed from a practical perspective.

A novel cryptocurrency price trend forecasting model based on LightGBM

2020 ◽  
Vol 32 ◽  
pp. 101084 ◽  
Author(s):  
Xiaolei Sun ◽  
Mingxi Liu ◽  
Zeqian Sima
Keyword(s):  
Forecasting Model ◽  
Price Trend ◽  
Model Based ◽  
Trend Forecasting

scholarly journals Mine Gas Concentration Forecasting Model Based on an Optimized BiGRU Network

ACS Omega ◽  
2020 ◽  
Vol 5 (44) ◽  
pp. 28579-28586
Author(s):  
Rong Liang ◽  
Xintan Chang ◽  
Pengtao Jia ◽  
Chengyixiong Xu
Keyword(s):  
Forecasting Model ◽  
Gas Concentration ◽  
Model Based ◽  
Mine Gas
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