I have a time series with predictive temperature and real temperature. I need to decompose a time series into components. Some components from time series are level, trend,seasonality and one component called noise.
Where the components are added together as follows:
Time series = Level + Trend + Seasonality + Noise
Noise=Time series- seasonality –trend
The purpose of decomposing the time series is to easily analyze the behavior of the results over time
This is the original predicted temperature time series which shows a nonlinear trend
I tried to remove the trend, this is the result it provided
Here is the code I have used
clear
close all
load StepOutTemp__Estacion.mat
%Señal de Temperatura Predicha
x=1:1:100
ptemp=cell2mat(matriz_results(88277:88376,3:3))'
ordsignal=6
coeficient=polyfit(x,ptemp,6)
[p,s,mu]=polyfit(x,ptemp,ordsignal)
f_y=polyval(p,x,[],mu)
rtrend=ptemp-f_y
subplot(2,1,1)
plot(x,ptemp),grid
title('Tiempo de Predicción adelante: 60 minutos'),ylabel('Temperatura en °C')
subplot(2,1,2)
plot(x,rtrend),grid
xlabel('Muestras de Tiempo'),ylabel('Temperatura en °C')
title('Tiempo de Predicción adelante: 60 minutos-Sin Tendencia')
The ordsignalis the orden a function.
Remove trends from Data
https://www.mathworks.com/help/signal/ug/remove-trends-from-data.html
I need to show a similar result or if maybe there is a tool to decompose a time series into
components. I have not been able to estimate noise or residual components and seasonality. Thanks in advance for your help.
