Question

对于群集可用空间（以GB为单位），我有近2年的每日时间序列数据。我想用facebook的先知来做未来的预测。一些预测具有负值。由于负值没有意义，我发现使用物流增长模型的承载能力有助于消除带有上限值的负面预测。我不确定这是否适用于这种情况以及如何获得我的时间序列的上限值。请帮忙，因为我是新来的，很困惑。我使用的是Python 3.6

import numpy as np
import pandas as pd
import xlrd
import openpyxl
from pandas import datetime
import csv
import matplotlib.pyplot as plt
plt.style.use('fivethirtyeight')
from fbprophet import Prophet
import os
import sys
import signal




df = pd.read_excel("Data_Per_day.xlsx")
df1=df.filter(['cluster_guid','date','avail_capacity'],axis=1)
uniquevalues = np.unique(df1[['cluster_guid']].values)

for id in uniquevalues:


newdf = df1[df1['cluster_guid'] == id]

    newdf1=newdf.groupby(['cluster_guid','date'],as_index=False['avail_capacity'].sum()
        #newdf11=newdf.groupby(['cluster_guid','date'],as_index=False)['total_capacity'].sum()
        #cap[id]=newdf11['total_capacity'].max()
        #print(cap[id])
    newdf1.set_index('cluster_guid', inplace=True)

    newdf1.to_csv('my_csv.csv', mode='a',header=None)
with open('my_csv.csv',newline='') as f:
    r = csv.reader(f)
    data = [line for line in r]
with open('my_csv.csv','w',newline='') as f:
    w = csv.writer(f)
    w.writerow(['cluster_guid','DATE_TAKEN','avail_capacity'])
    w.writerows(data)





in_df = pd.read_csv('my_csv.csv', parse_dates=True, index_col='DATE_TAKEN' )

in_df.to_csv('my_csv.csv')

dfs= pd.read_csv('my_csv.csv')
uni=dfs.cluster_guid.unique()

while True:
    try:
        print(" Press Ctrl +C  to exit   or  enter the cluster guid to be forcasted")
        i=input('Please enter the cluster  guid')
        if i not in uni:
            print( 'Please  enter a  valid cluster  guid')
            continue
        else:

        dfs1=dfs.loc[df['cluster_guid'] == i]
        dfs1.drop('cluster_guid', axis=1, inplace=True)
        dfs1.to_csv('dataframe'+i+'.csv', index=False)
        dfs2=pd.read_csv('dataframe'+i+'.csv')
        dfs2['DATE_TAKEN'] = pd.DatetimeIndex(dfs2['DATE_TAKEN'])
        dfs2 = dfs2.rename(columns={'DATE_TAKEN': 'ds','avail_capacity': 'y'})
        my_model = Prophet(interval_width=0.99)
        my_model.fit(dfs2)
        future_dates = my_model.make_future_dataframe(periods=30, freq='D')
        forecast = my_model.predict(future_dates)
        print(forecast[['ds', 'yhat', 'yhat_lower', 'yhat_upper']])
        my_model.plot(forecast,uncertainty=True)
        my_model.plot_components(forecast)
        plt.show()

        os.remove('dataframe'+i+'.csv')
        os.remove('my_csv.csv')


except KeyboardInterrupt: 
    try:
        os.remove('my_csv.csv')
    except OSError:
        pass
    sys.exit(0)

Answer 1

0阶Box-Cox变换可以完成技巧。步骤如下：

1. Add 1 to each values (so as to avoid log(0))
2. Take natural log of each value
3. Make forecasts
4. Take exponent and subtract 1

这样，您将不会得到负面的预测。 log也具有将乘法季节性转换为加法形式的良好属性。

使用facebook先知的负面预测

1 个答案: