养老床位需求量预测模型

Forecasting Model for the Demand of the Old-Age Bed

摘要: 当今中国已进入老龄化社会，根据国际上对于人口老龄化的定义，当一个国家或地区的60岁以上人口超过10%，意味着这个国家进入严重老龄化。而作为老年人未来生活起居重要保障的养老服务设施却没有跟上人口老龄化的速度，从而解决养老服务问题也越来越突出。本文以养老服务床位数量预测的问题进行研究，以题目所给的数据为基础，选择适合的科学原理和预测方法，对数据进行处理、挖掘，建立预测模型，然后再对模型处理和分析，研究并作出养老床位预测模型，并给出合理化建议，为企业和政府提供便利条件和可持续发展的商业模式。本文采用最小二乘支持向量机的方法，以机器学习理论与统计理论为基础，运用支持向量机的方法进行了完整的建模工作。

Abstract: China is now in the population aging, and according to the international definition of the population aging, when a country or region has more than 10 percent of its population over the age of 60, the country is getting significantly older. However, as an important guarantee for the future life of the elderly, the old-age service facilities have not kept up with the speed of population aging. Thus, the problem of providing for the aged becomes more and more prominent. In this paper, the problem of forecasting the number of old-age service beds is studied. Based on the data given in the topic, the appropriate scientific principles and forecasting methods are selected, the data are processed and mined, and the forecasting model is established. Then, the model is processed and analyzed, the forecasting model of old-age service beds is studied and made, and reasonable suggestions are given to provide convenient conditions and sustainable business models for enterprises and governments. In this paper, the method of least squares support vector machine is adopted. Based on machine learning theory and statistical theory, a complete modeling work is carried out by using support vector machine.