I would like to plot the historic forecasts of intraday 30 min data form the SPY. Data is here.
I first plot the forecast fitted from a time window of the last 10 days. h= 13 is the number of 30 minute intervals on a trading day.
require(forecast)
a.win <- window(spy.close,start = end(spy.close)[1]-10*1440*60,end =end(spy.close)[1])
a.fit <- auto.arima(a.win)
a.pred <- forecast(a.fit, h=13)
plot(a.pred, type="l", xlab="period", ylab="price",
main="Overlay historic forecasts & actual prices")
Then I shift the 10 day window for one day 5 times, fit the model, and plot the forecasted mean on each run of the loop.
for (j in seq(1, 5, by=1)) { ## Loop to overlay early forecasts
result1 <- tryCatch({
b.end <- end(spy.close)[1]-j*1440*60 ## Window the time series
b.start <- b.end[1]-10*1440*60
b.window <- window(spy.close, start=b.start, end=b.end)
b.fit <-auto.arima(b.window)
b.pred <- forecast(b.fit, h=13)
lines(b.pred$mean, col="green", lty="dashed" )
}, error = function(e) {return(e$message)} ) ## Skip Errors
}
But something is messing up the time axis. So I figured that the problem is that the data is irregular (trading hours from 9.30 to 16, weekends markets are closed), but I could not find a suitable solution (even in the forecast package doc there is no mention of intraday time intervals). Hope that someone can help...
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I'm new to time series, but am trying to do some simple analyses for monthly water quality values across multiple lakes. I have 20 water bodies, and monthly Dissolved Oxygen values for 2 years and 7 months. I am able to successfully run a nonparametric Mann-Kendall and Theil-Sen estimates for each water body across the time period, but am struggling to figure out how to run similar tests while taking into account seasonality.
Seasonal Mann-Kendalls seem to be working, but seasonal Theil-Sen estimates seem to fail when using sea.sens.slope() from the trend package. I am receiving the error "number of items to replace is not a multiple of replacement length". I THINK i'm getting this because I only have 7 months of data on the final year and not a full 12.
Eg:
library(trend)
library(Kendall)
library(dplyr)
#Sample Dataframe with 20 columns (waterbodies) and 31 months
df <- as.data.frame(matrix(rnorm(31*20,10,1),ncol=20))
#Creating a function to run MK and sen's Slopes on each water body
series.func <- function(x) {
c(mk = MannKendall(x), ss = sens.slope(x))
}
#running the function on each column
results <- df %>% ts(start = c(2019, 01),
end = c(2022, 07),
frequency = 12) %>% lapply(., series.func)
This gives me a list with MK and Theil Sen results for each water body, however when I attempt to do the same thing with seasonal Mann-Kendall and seasonal sen's slopes I get the error I mentioned
#Same function but with seasonal MK and Theil Sen
series.func <- function(x) {
c(smk = smk.test(x), seass = sea.sens.slope(x)
)
}
#running the function on each column
results <- df %>% ts(start = c(2019, 01),
end = c(2022, 07),
frequency = 12) %>% lapply(., series.func)
My questions are:
Is it possible to run sea.sens.slope() when I only have 7 months of data for my third year?
If it is possible, what exactly determines what a "season" is? My data is tropical and there aren't 4 distinct "seasons"
Thank you!
I am looking to forecast my time series. I have the following period daily data 2021-Jan-1 to 2022-Jul-1.
So I have a column of observations for each day.
what I tried so far:
d1=zoo(data, seq(from = as.Date("2021-01-01"), to = as.Date("2022-07-01"), by = 1))
tsdata <- ts(d1, frequency = 365)
ddata <- decompose(tsdata, "multiplicative")
I get following error here:
Error in decompose(tsdata, "multiplicative") :
time series has no or less than 2 periods
From what i have read it seems like because I do not have two full years? is that correct? I have tried doing it weekly as well:
series <- ts(data, frequency = 52, start = c(2021, 1))
getting the same issue.
How do I go about it without having to extend my dataset to two years since I do not have that, and still being able to decompose it?
Plus when I am actually trying to forecast it, it isn't giving me good enough forecast:
Plot with forecast
My data somewhat resembles a bell curve during that period. so is there a better fitting timeseries model I can apply instead?
A weekly frequency for daily data should have frequency = 7, not 52. It's possible that this fix to your code will produce a model with a seasonal term.
I don't think you'll be able to produce a time series model with annual seasonality with less than 2 years of data.
You can either produce a model with only weekly seasonality (I expect this is what most folks would recommend), or if you truly believe in the annual seasonal pattern exhibited in your data, your "forecast" can be a seasonal naive forecast that is simply last year's value for that particular day. I wouldn't recommend this, because it just seems risky, and I don't really see the same trajectory in your screenshot over 2022 that's apparent in 2021.
decompose requires two full cycles and that a full cycle represent 1 time unit. ts class can't use Date class anyways. To use frequency 7 we must use times 1/7th apart such as 1, 1+1/7, 1+2/7, etc. so that 1 cycle (7 days) covers 1 unit. Then just label the plot appropriately rather than using those times on the X axis. In the code below use %Y in place of %y if the years start in 19?? and end in 20?? so that tapply maintains the order.
# test data
set.seed(123)
s <- seq(from = as.Date("2021-01-01"), to = as.Date("2022-07-01"), by = 1)
data <- rnorm(length(s))
tsdata <- ts(data, freq = 7)
ddata <- decompose(tsdata, "multiplicative")
plot(ddata, xaxt = "n")
m <- tapply(time(tsdata), format(s, "%y/%m"), head, 1)
axis(1, m, names(m))
I have daily electric load data from 1-1-2007 till 31-12-2016. I use ts() function to load the data like so
ts_load <- ts(data, start = c(2007,1), end = c(2016,12),frequency = 365)
I want to remove the yearly and weekly seasonality from my data, to decompose the data and remove the seasonality, I use the following code
decompose_load = decompose(ts_load, "additive")
deseasonalized = ts_load - decompose_load$seasonal
My question is, am I doing it right? is this the right way to remove the yearly seasonality? and what is the right way to remove the weekly seasonality?
A few points:
a ts series must have regularly spaced points and the same number of points in each cycle. In the question a frequency of 365 is specified but some years, i.e. leap years, would have 366 points. In particular, if you want the frequency to be a year then you can't use daily or weekly data without adjustment since different years have different numbers of days and the number of weeks in a year is not integer.
decompose does not handle multiple seasonalities. If by weekly you mean remove the effect of Monday, of Tuesday, etc. and if by yearly you mean remove the effect of being 1st of the year, 2nd of the year, etc. then you are asking for multiple seasonalities.
end = c(2017, 12) means the 12th day of 2017 since frequency is 365.
The msts function in the forecast package can handle multiple and non-integer seasonalities.
Staying with base R, another approach is to approximate it by a linear model avoiding all the above problems (but ignoring correlations) and we will discuss that.
Assuming the data shown reproducibly in the Note at the end we define the day of week, dow, and day of year, doy, variables and regress on those with an intercept and trend and then construct just the intercept plus trend plus residuals in the last line of code to deseasonalize. This isn't absolutely necessary but we have used scale to remove the mean of trend in order that the three terms defining data.ds are mutually orthogonal -- Whether or not we do this the third term will be orthogonal to the other 2 by the properties of linear models.
trend <- scale(seq_along(d), TRUE, FALSE)
dow <- format(d, "%a")
doy <- format(d, "%j")
fm <- lm(data ~ trend + dow + doy)
data.ds <- coef(fm)[1] + coef(fm)[2] * trend + resid(fm)
Note
Test data used in reproducible form:
set.seed(123)
d <- seq(as.Date("2007-01-01"), as.Date("2016-12-31"), "day")
n <- length(d)
trend <- 1:n
seas_week <- rep(1:7, length = n)
seas_year <- rep(1:365, length = n)
noise <- rnorm(n)
data <- trend + seas_week + seas_year + noise
you can use the dsa function in the dsa package to adjust a daily time series. The advantage over the regression solution is, that it takes into account that the impact of the season can change over time, which is usually the case.
In order to use that function, your data should be in the xts format (from the xts package). Because in that case the leap year is not ignored.
The code will then look something like this:
install.packages(c("xts", "dsa"))
data = rnorm(365.25*10, 100, 1)
data_xts <- xts::xts(data, seq.Date(as.Date("2007-01-01"), by="days", length.out = length(data)))
sa = dsa::dsa(data_xts, fourier_number = 24)
# the fourier_number is used to model monthly recurring seasonal patterns in the regARIMA part
data_adjusted <- sa$output[,1]
I'm trying to decompose my data to see what the trend and seasonality effects are. I have 4 months of data, recorded daily. Data looks like:
date amount
11/1/2000 1700
11/2/2000 11087
11/3/2000 11248
11/4/2000 13336
11/5/2000 18815
11/6/2000 8820
11/7/2000 7687
11/8/2000 5514
11/9/2000 9591
11/10/2000 9676
11/11/2000 14782
11/12/2000 18554
And so forth to the end of Feb 2001. I read in the data like so and generate a timeseries object:
myvector <- read.table("clipboard", sep="\t", header=T)
myts <- ts(myvector$amount, start=c(2000,11), frequency=52)
I'm very confused as to how to read this data in as a time series object. The data is recorded daily, but if I use frequency=365, then try
fit <- stl(myts2, s.window="periodic")
I get:
Error in stl(myts2, s.window = "periodic") :
series is not periodic or has less than two periods
Every example I find does the object casting with multiple years worth of data. Is this not possible in my case?
I know the next steps for plotting the trend and decomposition are:
fit <- stl(myts, s.window="periodic")
plot(fit)
Try seasonal differencing, which is similar to regular differencing except is applied over different periods:
An example:
data(austres)
plot(austres)
seasonal <- diff(austres, lag = 12, differences = 1)
plot(seasonal)
d.seasonal <- diff(seasonal, differences = 2)
plot(d.seasonal)
Now you've made stationary the seasonal component of the time series.
I've been trying to develop an ARIMA model to forecast wind speed values. I have a four year data series (from january 2008 until december 2011). The series presents 10 minute data, which means that in a day we have 144 observations. Well, I'm using the first three years (observations 1 to 157157) to generate the model and the last year to validate the model.
The thing is I want to update the forecast. On other words, when one forecast ends up, more data is added to the dataset and another forecast is performed. But the result seems like I had just lagged the original series. Here's the code:
#1 - Load data:
z=read.csv('D:/Faculdade/Mestrado/Dissertação/velocidade/tudo_10m.csv', header=T, dec=".")
vel=ts(z, start=c(2008,1), frequency=52000)
# 5 - ARIMA Forecasts:
library(forecast)
n=157157
while(n<=157200){
amostra <- vel[1:n] # Only data until 2010
pred <- auto.arima(amostra, seasonal=TRUE,
ic="aicc", stepwise=FALSE, trace=TRUE,
approximation=TRUE, xreg=NULL,
test="adf",
allowdrift=TRUE, lambda=NULL, parallel=TRUE, num.cores=4)
velpred <- arima(pred) # Is this step really necessary?
velpred
predvel<- forecast(pred, h=12) # h means the forecast steps ahead
predvel
plot(amostra, xlim=c(157158, n), ylim=c(0,20), col="blue", main="Previsões e Observações", type="l", lty=1)
lines(fitted(predvel), xlim=c(157158, n), ylim=c(0,20), col="red", lty=2)
n=n+12
}
But when it plot the results (I couldn't post the picture here), it exhibits the observed series and the forecasted plot, which seems just the same as the observed series, but one step lagged.
Can anyone help me examining my code and/or giving tips on how to get the best of my model? Thanks! (Hope my English is understandable...)