I have a time series of daily temperature from 1960 to 2015, and would like to forecast for 2016. My goal is to use a simple method while still be able to capture the trend and seasonality of the data over time. (I actually only care about the weather condition from May to August, but not sure if I can just use data from May to August in the past years to get a convincing forecast, so I am forecasting for a year)
I tried auto.arima, it suggested an order of (2,0,1), but the results seemed pretty bad (see plot)[forecasts from arima][1]
In addition, I tried HoltWinters smoothing method and got a seemingly reasonable result. However I don't know if this method is good at forecasting temperature.
[forecast from HW][2]
I too am hesitant to provide an answer without more information; however, I will suggest that for someone without a lot of experience in time series forecasting, the auto.arima function in the "forecast" package is excellent. It has a built in optimization that searches for the best ARIMA order (p,d,q). Here is some example code:
install.packages("forecast")
library(forecast)
set.seed(1234)
tsdatav <- (seq(1:300) + rnorm(300, 1000, 10))
myts <- ts(tsdatav, frequency = 365, start = c(2017, 6))
mytsfit <- auto.arima(myts)
mytsfit #to my example data, fit an ARIMA(5,1,0) with drift
mytsforecast <- forecast(mytsfit, 50, level = c(80, 95))
plot(mytsforecast)
Notice that in the forecast function, you can set how many periods you want to forecast out as well as confidence intervals (in addition to the point forecast).
Duke has an excellent website on ARIMA forecasting at https://people.duke.edu/~rnau/411arim.htm
Again, this is just one suggestion. There are many forecasting approaches that work better than others given problem specifics.
Related
I've tried searching but couldn't find a specific answer to this question. So far I'm able to realize that Time Series Forecasting is possible using SVM. I've gone through a few papers/articles who've performed the same but didn't mention any code, instead explained the algorithm (which I didn't quite understand). And some have done it using python.
My problem here is that: I have a company data(say univariate) of sales from 2010 to 2017. And I need to forecast the sales value for 2018 using SVM in R.
Would you be kind enough to simply present and explain the R code to perform the same using a small example?
I really do appreciate your inputs and efforts!
Thanks!!!
let's assume you have monthly data, for example derived from Air Passengers data set. You don't need the timeseries-type data, just a data frame containing time steps and values. Let's name them x and y. Next you develop an svm model, and specify the time steps you need to forecast. Use the predict function to compute the forecast for given time steps. That's it. However, support vector machine is not commonly regarded as the best method for time series forecasting, especially for long series of data. It can perform good for few observations ahead, but I wouldn't expect good results for forecasting eg. daily data for a whole next year (but it obviously depends on data). Simple R code for SVM-based forecast:
# prepare sample data in the form of data frame with cols of timesteps (x) and values (y)
data(AirPassengers)
monthly_data <- unclass(AirPassengers)
months <- 1:144
DF <- data.frame(months,monthly_data)
colnames(DF)<-c("x","y")
# train an svm model, consider further tuning parameters for lower MSE
svmodel <- svm(y ~ x,data=DF, type="eps-regression",kernel="radial",cost=10000, gamma=10)
#specify timesteps for forecast, eg for all series + 12 months ahead
nd <- 1:156
#compute forecast for all the 156 months
prognoza <- predict(svmodel, newdata=data.frame(x=nd))
#plot the results
ylim <- c(min(DF$y), max(DF$y))
xlim <- c(min(nd),max(nd))
plot(DF$y, col="blue", ylim=ylim, xlim=xlim, type="l")
par(new=TRUE)
plot(prognoza, col="red", ylim=ylim, xlim=xlim)
I'm new to R but have some experience with ARIMA models. Now I wanted to learn a bit about neural networks for forecasting.
I tried to repeat the procedure from Rob's post. It worked great for the data set he used. It also worked great for imaginary datasets I created.
But then I tried to use real-life data (revenue data for 7 years monthly) and the resulting forecasts are strangely flat. My code:
read.csv("Revenue.csv",header=TRUE)
x <-read.csv("Revenue.csv",header=TRUE)
y<-ts(x,freq=12,start=c(2011,1))
(fit<-nnetar(y))
fcast <- forecast(fit, PI=TRUE, h=20, bootstrap=TRUE)
autoplot(fcast)
The result is an almost straight line (attached as picture 1). That strikes me as odd, because the trend has been positive so far: there was a revenue growth of more than 100% every year. Still the result of nnetar is that the revenue will stabilise. How is that possible?
As a comparison I used Auto.arima for the same data set (picture 2). It shows a clear upward trend.
One suggestion, even if its hard to help without the data sample.
It appears than nnetar is not capturing very well the trend in your data.
Probably you could try to use a trend as external regressors ( xreg argument)
For example for a deterministic trend.
Trend=seq(from=start, to=end, by=1)
(fit <- nnetar(y, xreg=Trend))
(f <- forecast(fit,h=h, xreg=seq(from=end, to=end+h, by=1))
An alternative would be to use more lag or seasonal lags (p and P argument in your nnetar model)
I use Hyndman's forecast package to produce a somewhat accurate tbats forecast at the weekly level, but I have significant errors on holidays. How can I include holidays in the model? Also, Arima has been shown to fit my weekly data poorly. So holidays would have to be added in a non-arima way.
I have seen two solutions. One https://robjhyndman.com/hyndsight/dailydata/ shows how to add holidays as dummy variables with fourier terms. The problem is dummy variables take the form of 1 or 0. I know that different holidays have different effects that a 1 or 0 would not capture. Black Friday, for example, is very different from Chinese New Year.
Another solution is have seen is here https://robjhyndman.com/hyndsight/forecast7-part-2/ where covariate nnetr change is used as an alternative to auto.arima with seasonal dummy variables. The problem is I don't see how to write the R code to input my holidays. An example would be useful.
The benchmark for time series modeling for use by official statistics agencies is x13-arima-seats by the US Census bureau. It deals with seasonal effects as well as with "parametric" holidays including, say, the Chinese New Year as well as Easter.
The functionality is available in R via the seasonal package which installs and uses the underlying x13-arima-seats binary.
And there is also a full-feature interactive website giving access to most-if-not-all features.
Have you read about Facebook's prophet package?
Haven't used it but from reading the documentation, it seems like a quick implementation that also accounts for holidays:
https://cran.r-project.org/web/packages/prophet/prophet.pdf
Implements a procedure for forecasting time series data based on
an additive model where non-linear trends are fit with yearly and weekly
seasonality, plus holidays [...]
https://cran.r-project.org/web/packages/prophet/vignettes/quick_start.html
The following did everything I needed it to do.
k=23
#forecast holidays
#bool list of future holidays
holidayf <- c(0,0,0,0,0,1,0,0,0,1,1,1,1,1,0,0,0)
h <- length(holidayf)
#given holidays
holiday <- df[,2]
y <- ts(df[,1],start = 2011,frequency = 52)
z <- fourier(y, K=k)
zf <- fourier(y, K=k, h=h)
fit <- auto.arima(y, xreg=cbind(z,holiday), seasonal=FALSE)
fc <- forecast(fit, xreg=cbind(zf,holidayf), h=h)
fc %>% autoplot()
summary(fit)
To solve the problem of different holidays having different effect, I simply added additional holiday dummy variables. For example, you can make a vector of good holidays and a vector of bad holidays and cbind them then put them in xreg. I did not show this in the above code, but it is straight forward.
i'm new to R, so I'm having trouble with this time series data
For example (the real data is way larger)
data <- c(7,5,3,2,5,2,4,11,5,4,7,22,5,14,18,20,14,22,23,20,23,16,21,23,42,64,39,34,39,43,49,59,30,15,10,12,4,2,4,6,7)
ts <- ts(data,frequency = 12, start = c(2010,1))
So if I try to decompose the data to adjust it
ts.decompose <- decompose(ts)
ts.adjust <- ts - ts.decompose$seasonal
ts.hw <- HoltWinters(ts.adjust)
ts.forecast <- forecast.HoltWinters(ts.hw, h = 10)
plot.forecast(ts.forecast)
But for the first values I got negative values, why this is happening?
Well, you are forecasting the seasonally adjusted time series, and of course the deseasonalized series ts.adjust can already contain negative values by itself, and in fact, it actually does.
In addition, even if the original series contained only positive values, Holt-Winters can yield negative forecasts. It is not constrained.
I would suggest trying to model your original (not seasonally adjusted) time series directly using ets() in the forecast package. It usually does a good job in detecting seasonality. (And it can also yield negative forecasts or prediction intervals.)
I very much recommend this free online forecasting textbook. Given your specific question, this may also be helpful.
I am using R for sometime now and some days ago I found a very interesting function which made a prediction on a given time series. It just took the data from the known time series and applied it on a given period, but it kept the pattern. The problem is that I lost it. I am sure it was a sort of HoltWinters. I am trying two days to find something, but till now without success. Could someone please give me a hand on this!
Just use predict:
# Assuming you have some timeseries data in myts
hw <- HoltWinters(myts)
predict(hw, 10) # predict 10 periods ahead
You can use forecast.HoltWinters
#Model creation
fit <- HoltWinters(ts.data,gamma=FALSE)
#Load forecast package
require(forecast)
#Apply model into forecast
forecast(fit)