本文選題：老齡人口 + 灰色預(yù)測��； 參考：《哈爾濱工業(yè)大學(xué)》2017年碩士論文

【摘要】：我國人口老齡化問題正在日益嚴(yán)重,由于人口結(jié)構(gòu)的老化,各種社會問題開始顯現(xiàn);由于老齡人口的比例的持續(xù)增加,使得青壯年勞動力的比例持續(xù)下降,對國家和社會的發(fā)展產(chǎn)生了影響。更重要的是,我國目前面臨的是“未富先老”的不利局面。老齡人口增加的同時,并沒有完善和足夠的政策體系來支持,使得家庭負(fù)擔(dān)和社會負(fù)擔(dān)持續(xù)加重。于是,對老齡人口的精確預(yù)測的重要性凸顯。如今對于人口預(yù)測的方法可謂多種多樣,各種方法都有各自的優(yōu)勢和不足。本文將研究灰色預(yù)測模型,并利用該模型和優(yōu)化后的該模型對老齡人口進(jìn)行預(yù)測,具體研究分為三個部分:第一,本文將首先介紹灰色模型的相關(guān)理論,并給出原始的灰色預(yù)測模型,同時給出預(yù)測的步驟。并且本文將給出幾種預(yù)測精度的檢驗方法。對模型精度進(jìn)行檢驗并提出優(yōu)化方向。第二,根據(jù)對原始模型的研究,本文將從幾個方向?qū)δＰ吞岢鰞?yōu)化。就序列光滑性給出函數(shù)變換提高光滑性的改進(jìn);就模型背景值方向給出背景值參數(shù)化改進(jìn);也給出利用灰色組合預(yù)測模型提高預(yù)測精度的方法。第三,本文將就2005年至2014年的老齡人口數(shù)進(jìn)行模型的模擬。分別將數(shù)據(jù)用原始模型及改進(jìn)后的模型進(jìn)行測試,比較各個模型的模擬精度。選擇精度最高的優(yōu)化方式給出后續(xù)十年老齡人口的預(yù)測值。
[Abstract]:The problem of aging population in our country is becoming more and more serious. Due to the aging of the population structure, various social problems are beginning to appear. As the proportion of the aged population continues to increase, the proportion of the young and middle-aged labor force continues to decline. It has had an impact on the development of the country and society. More importantly, China is now facing the disadvantage of being old before becoming rich. While the aging population increases, there is no perfect and sufficient policy system to support the family burden and social burden. Therefore, the importance of accurate prediction of the elderly population is highlighted. Nowadays, there are a variety of methods for population forecasting, and each method has its own advantages and disadvantages. This paper will study the grey prediction model, and use this model and the optimized model to predict the aging population. The specific research is divided into three parts: first, this paper will first introduce the relevant theories of the grey model. The original grey prediction model is given, and the steps of prediction are also given. And this paper will give several methods to test the prediction accuracy. The accuracy of the model is tested and the optimization direction is proposed. Secondly, according to the study of the original model, this paper will optimize the model from several directions. The improvement of function transformation to improve smoothness, the parameterization of background value to the direction of background value of the model, and the method of improving prediction accuracy by using grey combination forecasting model are given. Thirdly, this paper will simulate the aging population from 2005 to 2014. The data were tested with the original model and the improved model, and the simulation accuracy of each model was compared. Select the most accurate optimization method to give the prediction value of the aging population for the next ten years.
【學(xué)位授予單位】：哈爾濱工業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：C924.24;N941.5

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