实验目的

实验装置

【部分】实验原理

【部分】实验数据处理

借助Python

插个题外话，如果数据比较多的话，或者说需要额外的数据处理的话

pip install numpy
pip install matplotlib
pip install pandas

import numpy as np
import matplotlib as plt
import pandas as pd
#读取数据
file_path = open('地址')
file_date = pd.read_csv(file_path)
file_date.head()##tail()

#数据预处理
file_date.duplicated()#重复值检测
file_date = file_date.drop_duplicates()#删除重复值
file_date = file_date.dropna()#删除缺失值
date_new = np.array([])#创建空数组
date = file_date['列名'].values#处理一列数据
for i in date:
    date_new = np.append(date_new,np.array(i[:-2]))
date = date_new.astype(np.float64)#转换数据类型
file_date.loc[:,'列名'] = date
file_date

借助MATLAB

如果你的数据量不够的话，可以用神经网络来预测一下
以下是我之前的一个项目的，改一改变量就行

quantity=[33.00 ,33.23 ,33.90 ,35.01 ,36.55 ,38.53 ,40.92 ,43.73 ,46.94 ,50.56 ,54.57 ,58.98 ,63.76 ,68.92 ,74.45 ,80.35 ,86.60 ,93.20 ,100.15 ,107.44 ,115.06 ,123.00 ,136.78 ,159.08 ,185.65 ,212.24 ,234.61 ,248.50 ,254.50 ,274.40 ,297.08 ,320.13 ,344.17 ,369.84 ,397.76 ,428.56 ,462.87 ,501.30 ,544.50 ,615.18 ,683.60 ,722.58 ,757.27 ,788.14 ,815.64 ,840.23 ,862.36 ,882.48 ,901.06 ,918.55 ,935.40 ,951.22 ,965.47 ,978.38 ,990.18 ,1001.10 ,1011.29 ,1020.70 ,1029.23 ,1036.80]; 

x=year; 
y=quantity; 
net=newff(x,y,2); %Generate a BP network with one neuron for both input and output, and 2 neurons for the hidden layer. 
net.trainParam.epochs = 1000; % Number of training sessions, this file is set to 1000 (the maximum number of training sessions). 
net.trainParam.goal = 0.1; % Minimum error of the training target, this file is set to 0.1. 
net.trainParam.lr = 0.01; % Learning rate, this file is set to 0.01. 
net=train(net,x,y); %Training Network 
%%%forecasting their quantities for 1962-2021 
quantity_pre=sim(net,year); 
plot(year,quantity,'*b','MarkerSize',8);hold on 
plot(year,quantity_pre,'or','MarkerSize',8);hold on 
plot(year,quantity,'b','LineWidth',2);hold on 
plot(year,quantity_pre,'r','LineWidth',2);grid on 
legend('Real Data','Theoretical Data') 
xlabel('year') 
ylabel('quantity') 
title('BP neural network forecasting curve') 
%%%projected numbers between 2021-2049. 
Year_pre=2021:2049; 
quantity_pre_BP=sim(net,Year_pre)

【部分】实验分析与讨论

【部分】实验心得与建议

参考文献

【更多内容】

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原版【全文】文件在这里【详细】

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