Apparatus and method for forecasting technology demand using cumulative and soapless diffusion models

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• 2. 使用阵列空间伪灵敏度编码技术的多次激发扩散加权成像的设备和方法 [P] . 中国专利： CN107430179A . 2017-12-01
• 3. APPARATUS AND METHOD FOR ESTIMATING TECHNOLOGICAL DEMANDS USING CUMULATIVE AND NON-CUMULATIVE DIFFUSION MODELS [P] . 韩国专利： KR20100090882A . 2010-08-18

  机译： 使用累积和非累积扩散模型估算技术需求的装置和方法

• 4. Demand forecast model evaluation device, demand forecast model evaluation method and demand forecast model evaluation program [P] . JP6844304B2 . 2021-03-17

  机译： 需求预测模型评估装置，需求预测模型评估方法和需求预测模型评估计划

• 5. Demand forecasting device, control device, conversion device, forecast model creation device, conversion model creation device, demand forecast method and program [P] . 日本专利： JP2020165616A . 2020-10-08

  机译： 需求预测装置，控制装置，转换装置，预测模型创建装置，转换模型创建装置，需求预测方法和程序

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• 11. "A Karnel Regression of Phillips' Data" Abstract: Economists have assumed that the Phillips curve, which shows a positive (negative) relation between inflation and the output ratio (unemployment rate), may be mapped off the aggregate demand -aggregate supply apparatus. The paper shows that the Phillips curve requires that unlikely restrictions be put on the form of the aggregate supply and aggregate demand curves. In this case, it is inappropriate to treat data on inflation and capacity utilization as the basis for estimating an underlying formal model. The paper therefore uses a nonparametric, data-driven method to describe the data. This method, of kernel regression, shows the inflation-unemployment association in Phillips's sample to be negative on a global scale, yet irregular within particular ranges of unemployment. [O] . Nancy J. Wulwick, Y.P. Mack 100

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