I made a plot that predicts a time series. It was achieved wih this code:
forecast1 <- HoltWinters(ts, beta = FALSE, gamma = TRUE)
forecast2 <- forecast(forecast1, h = 60)
autoplot(forecast2)
Where 'ts' is a time series object.
So I would like to add another time series to compare predicted values with actual values, starting from my last actual observation. I achieved it with a classical plot, adding a line with actual time series. This are the plots I have:
How can I add this new line to my first plot?
Here is the simplest way to do it:
library(ggplot2)
library(forecast)
smpl1 <- window(AirPassengers, end = c(1952, 12))
smpl2 <- window(AirPassengers, start = c(1953, 1), end = c(1953,12))
hw <- HoltWinters(smpl1, beta = FALSE, gamma = TRUE)
forecast <- forecast(hw, h = 12)
autoplot(forecast) +
autolayer(smpl2, series="Data") +
autolayer(forecast$mean, series="Forecasts")
The autolayer command from the forecast package allows you to add layers involving time series and forecasts to existing plots.
Related
Recently, i use Savitzky-Golay in signal package for smoothing my data, but it is not work well. I hear that Perona-Malik is good smooth method for this task, however, i could not realize it. My question is that is it possible realize the task to smooth the data by using P& M model by using R.
Thanks
hees
Simple example.
library(signal)
bf <- butter(5,1/3)
x <- c(rep(0,15), rep(10, 10), rep(0, 15))
###
sg <- sgolayfilt(x) # replace at here
plot(sg, type="l")
lines(filtfilt(rep(1, 5)/5,1,x), col = "red") # averaging filter
lines(filtfilt(bf,x), col = "blue") # butterworth
p
I'm posting this because i've been having a little problem with my code. What i want to do is to make a forecast of COVID cases in a province for the next 30 days using the AUTOARIMA script. Everything is ok, but when i plot the forecast model, the date labels appears in increments of 25% (IE: 2020.2, 2020.4, etc), but i want to label that axis with a YMD format. This is my code:
library(readxl)
library(ggplot2)
library(forecast)
data <- read_xlsx("C:/Users/XXXX/Documents/Casos ARIMA Ejemplo.xlsx")
provincia_1 <- ts(data$Provincia_1, frequency = 365, start = c(2020,64))
autoarima_provincia1 <- auto.arima(provincia_1)
forecast_provincia1 <- forecast(autoarima_provincia1, h = 30)
plot(forecast_provincia1, main = "Proyeccion Provincia 1", xlab = "Meses", ylab = "Casos Diarios")
When i plot the forecast, this is what appears (with the problem i've stated before on the dates label)
The database is here:
https://github.com/pgonzalezp/Casos-Covid-provincias
Try to create a data.frame having on one column your predictions and in the other the daily dates. Then plot it.
Introduce your start and ending date as seen below, then at "by" argument, please check documentation from this link:
https://www.rdocumentation.org/packages/base/versions/3.6.2/topics/as.Date
df <- data.frame(
date=seq(as.Date("1999-01-01"), as.Date("2014-01-10"), by="6 mon"),
pred_val = forecast_provincia1
)
with(df, plot(date, pred_val ))
I got inspired from here:
R X-axis Date Labels using plot()
I am using the R. I am trying to use the "lines' command in ggplot2 to show the predicted values vs. the actual values for a statistical model (arima, time series). Yet, when I ran the code, I can only see a line of one color.
I simulated some data in R and then tried to make plots that show actual vs predicted:
#set seed
set.seed(123)
#load libraries
library(xts)
library(stats)
#create data
date_decision_made = seq(as.Date("2014/1/1"), as.Date("2016/1/1"),by="day")
date_decision_made <- format(as.Date(date_decision_made), "%Y/%m/%d")
property_damages_in_dollars <- rnorm(731,100,10)
final_data <- data.frame(date_decision_made, property_damages_in_dollars)
#aggregate
y.mon<-aggregate(property_damages_in_dollars~format(as.Date(date_decision_made),
format="%W-%y"),data=final_data, FUN=sum)
y.mon$week = y.mon$`format(as.Date(date_decision_made), format = "%W-%y")`
ts = ts(y.mon$property_damages_in_dollars, start = c(2014,1), frequency = 12)
#statistical model
fit = arima(ts, order = c(4, 1, 1))
Here were my attempts at plotting the graphs:
#first attempt at plotting (no second line?)
plot(fit$residuals, col="red")
lines(fitted(fit),col="blue")
#second attempt at plotting (no second line?)
par(mfrow = c(2,1),
oma = c(0,0,0,0),
mar = c(2,4,1,1))
plot(ts, main="as-is") # plot original sim
lines(fitted(fit), col = "red") # plot fitted values
legend("topleft", legend = c("original","fitted"), col = c("black","red"),lty = 1)
#third attempt (plot actual, predicted and 5 future values - here, the actual and future values show up, but not the predicted)
pred = predict(fit, n.ahead = 5)
ts.plot(ts, pred$pred, lty = c(1,3), col=c(5,2))
However, none of these seem to be working correctly. Could someone please tell me what I am doing wrong? (note: the computer I am using for my work does not have an internet connection or a usb port - it only has R with some preloaded packages. I do not have access to the forecast package.)
Thanks
Sources:
In R plot arima fitted model with the original series
R fitted ARIMA off by one timestep? pkg:Forecast
Plotting predicted values in ARIMA time series in R
You seem to be confusing a couple of things:
fitted usually does not work on an object of class arima. Usually, you can load the forecast package first and then use fitted.
But since you do not have acces to the forecast package you cannot use fitted(fit): it always returns NULL. I had problems with fitted
before.
You want to compare the actual series (x) to the fitted series (y), yet in your first attempt you work with the residuals (e = x - y)
You say you are using ggplot2 but actually you are not
So here is a small example on how to plot the actual series and the fitted series without ggplot.
set.seed(1)
x <- cumsum(rnorm(10))
y <- stats::arima(x, order = c(1, 0, 0))
plot(x, col = "red", type = "l")
lines(x - y$residuals, col = "blue")
I Hope this answer helps you get back on tracks.
I am foresting with combination of data sets from fpp2 package and forecasting function from the forecast package. Output from this forecasting is object list with SNAIVE_MODELS_ALL. This object contain data separate for two series, where first is Electricity and second is Cement.
You can see code below :
# CODE
library(fpp2)
library(dplyr)
library(forecast)
library(gridExtra)
library(ggplot2)
#INPUT DATA
mydata_qauselec <- qauselec
mydata_qcement <- window(qcement, start = 1956, end = c(2010, 2))
# Мerging data
mydata <- cbind(mydata_qauselec, mydata_qcement)
colnames(mydata) <- c("Electricity", "Cement")
# Test Extract Name
mydata1 <- data.frame(mydata)
COL_NAMES <- names(mydata1)
rm(mydata_qauselec, mydata_qcement)
# FORCASTING HORIZON
forecast_horizon <- 12
#FORCASTING
BuildForecast <- function(Z, hrz = forecast_horizon) {
timeseries <- msts(Z, start = 1956, seasonal.periods = 4)
forecast <- snaive(timeseries, biasadj = TRUE, h = hrz)
}
frc_list <- lapply(X = mydata1, BuildForecast)
#FINAL FORCASTING
SNAIVE_MODELS_ALL<-lapply(frc_list, forecast)
So my intention here is to put this object SNAIVE_MODELS_ALL into autoplot function in order to get two plots like pic below.
With code below I draw both plots separate, but my main intention is to do this with function autoplot and some function like apply or something similar, which can automatically draw this two chart like pic above.This is only small example in real example I will have maybe 5 or 10 charts.
#PLOT 1
P_PLOT1<-autoplot(SNAIVE_Electricity,main = "Snaive Electricity forecast",xlab = "Year", ylab = "in billion kWh")+
autolayer(SNAIVE_Electricity,series="Data")+
autolayer(SNAIVE_Electricity$fitted,series="Forecasts")
# PLOT 2
P_PLOT2<-autoplot(SNAIVE_Cement,main = "Snaive Cement forecast",xlab = "Year", ylab = "in millions of tonnes")+
autolayer(SNAIVE_Cement,series="Data")+
autolayer(SNAIVE_Cement$fitted,series="Forecasts")
#UNION PLOTS (PLOT 1 AND PLOT 2)
SNAIVE_PLOT_ALL<-grid.arrange(P_PLOT1,P_PLOT2)
So can anybody help me with this code ?
If I understand in a proper way, one of the difficulties with that problem is that each plot should have a specific title and y label. One of the possible solutions is to set the plot titles and y-lables as function arguments:
PlotForecast <- function(df_pl, main_pl, ylab_plt){
autoplot(df_pl,
main = main_pl,
xlab = "Year", ylab = ylab_plt)+
autolayer(df_pl,series="Data")+
autolayer(df_pl$fitted,series="Forecasts")
}
Prepare lists of the plot labels to be used with PlotForecast():
main_lst <- list("Snaive Electricity forecast", "Snaive Cement forecast")
ylab_lst <- list("in billion kWh", "in millions of tonnes")
Construct a list of plot-objects using a base Map() function:
PL_list <- Map(PlotForecast, df_pl = SNAIVE_MODELS_ALL, main_pl = main_lst,
ylab_plt= ylab_lst)
Then all we have to do is to call grid.arrange() with the plot list:
do.call(grid.arrange, PL_list)
Note, please, that main_lst and ylab_lst are created manually for demonstration purposes, but it is not the best way if you work with a lot of charts. Ideally, the labels should be generated automatically using the original SNAIVE_PLOT_ALL list.
I have written this code (see below) for my data frame kleaf.df to combine multiple plots of variable press_mV with each individual plot for unique ID
I need some help fitting curves to my plots. when i run this code i get the same fitted curve (the curve fitted for the first plot) on ALL the plots where i want each unique fitted curve on each unique plot.
thanks in advance for any help given
f <- function(t,a,b) {a * exp(b * t)}
par(mfrow = c(5, 8), mar = c(1,1,1,1), srt = 0, oma = c(1,6,5,1))
for (i in unique(kleaf.df$ID))
{
d <- subset(kleaf.df, kleaf.df$ID == i)
plot(c(1:length(d$press_mV)),d$press_mV)
#----tp:turning point. the last maximum value before the values start to decrease
tp <- tail(which( d$press_mV == max(d$press_mV) ),1)
#----set the end points(A,B) to fit the curve to
A <- tp+5
B <- A+20
#----t = time, p = press_mV
# n.b:shift by 5 accomadate for the time before attachment
t <- A:B+5
p <- d$press_mV[A:B]
fit <- nls(p ~ f(t,a,b), start = c(a=d$press_mV[A], b=-0.01))
#----draw a curve on plot using the above coefficents
curve(f(x, a=co[1], b=co[2]), add = TRUE, col="green", lwd=2)
}