1 Create time series
2 Seasonal decomposition
3 exponential model
4 ARIMA Model
5 Automatic prediction
The first step : Create time series
use ts() Function converts a number vector into a time series , In the form of
Part two : Seasonal decomposition
If the time series tends to increase , Seasonality , And irregular components , You can use the stl() function , If the multiple effects of time series can be used log(ts)
# Seasonal decomposition fit=stl(ts,s.window="period") plot(fit) # Add diagram monthplot(ts)
library(forest) seasonplt(ts)
Step three : exponential model
# Simple exponential smoothing model fit=HoltWinters(ts,beta=F,gamma=F) # have models level and trend
fit=HoltWinters(ts,beta=F)# have models levels,trend and seasonal compontents
fit=HoltWinters(ts)#predict accuracy library(forecast) accuracy(fit) #predict
next three future values library(forecast) forecast(fit,3) plot(forecast(fit,3))

©2019-2020 Toolsou All rights reserved,
Online troubleshooting HTTP Status code ——415 and 406415 Status code to background error ( Essence )2020 year 7 month 15 day Wechat applet import and include difference use PyMC3 Bayesian statistical analysis was performed ( code + example )python primitive -- lock Lock( Essence 2020 year 6 month 2 Daily update ) TypeScript Function explanation use VS2019 “Windows Desktop applications ” Module creation Win32 window ( Essence )2020 year 7 month 12 day webpack Use of common plug-ins ( Essence )2020 year 6 month 26 day C# Class library GUID Help class SpringMVC Frame in controller Layer gets the property value of the custom configuration file