I am working with some daily time series (unequally spaced) problem and want to detect the order of seasonality (and/or the frequency of the data if necessary).
I know there is seasonality from the time series plot and ACF plot. The features of seasonality is obvious. My code looks like the following:
plot(mydates, mydata, type="l")
Acf(mydata)
I tried to fit the data using auto.arima, but It returns a non-seasonal fit.
auto.arima(mydata)
Series: mydata, ARIMA(1,0,1) with zero mean, Coefficients: ....
I also tried function nsdiffs, and it doesn't work either.
nsdiffs(mydata)
Error in nsdiffs(tslist[[1]]) : Non seasonal data
nsdiffs(ts(mydata, frequency=90))
0
I technically cannot use the ts function because I don't really know the frequency of my data (which is what I intend to find out). But I tested anyway, using some random guess of the frequency. It returns 0 every time.
Could anyone help me with this?
Thank you!
Related
my problem is the following : I have a Landsat NDVI time series that is non-periodic/doesn't have a homogenous frequency. However, the error code I receive is
Error in stl(Yt, "periodic") : series is not periodic or has less than two periods
after having tried to convert my data into a timeseries without explicitely setting a frequency :
test_timeseries <-ts(test$nd, start = c(1984,4), end = c(2011,10)). when I try to calculate the frequency or deltat with the help of the functions frequency() or deltat(), it both leads to 1 - which I don't understand , as I have non-periodic data for nearly every month and not only once a year.
So my question is, how to set the frequency in this case and how to deal with this circumstance of non-periodicity ? It seems like, without setting a frequency, I cannot use the function bfast().
sorry if the answer is obvious, I'm very new to timeseries analyses.
Please read the help file. It helps. In this case, it describes the following argument.
season : the seasonal model used to fit the seasonal component and detect seasonal breaks (i.e. significant phenological change). There are three options: "dummy", "harmonic", or "none" where "dummy" is the model proposed in the first Remote Sensing of Environment paper and "harmonic" is the model used in the second Remote Sensing of Environment paper (See paper for more details) and where "none" indicates that no seasonal model will be fitted (i.e. St = 0 ). If there is no seasonal cycle (e.g. frequency of the time series is 1) "none" can be selected to avoid fitting a seasonal model.
So set season = "none" in bfast().
I am using the forecast package of R and I created a MA(1) model by using the ARIMA function. I plotted the time series itself ($x variable of the ma_model), the model itself ($fitted variable of the ma_model) and the residuals (residuals variable of the ma_model). Strangely the time series looks equal to the model altough there are nonegative residuals. Here is the code that I used:
library(forecast)
ma_model<-Arima(ts(generationData$Price[1:200]), order=c(0,1,0))
plot(ma_model$fitted, main = "Fitted")
plot(ma_model$x, main = "X")
plot(ma_model$residuals, main = "Residuals")
Here is the result
Basically the model can't be equal to the real time series especially when having residuals. Can anyone explain this to me? I'd appreciate every comment.
Update: I tried to use the order=c(0,0,20) so I have a MA(20) or AR(20) model (I am not sure which parameters stands for MA and AR). Now the fitted curve and the original time series look quite equal (but not exactly equal). Is this possible and usual? I'd appreciate every further comment.
Any comments on this issue?
I am not sure about your output, but from the code it seems that you just took the difference in the model, not the MA.
I think it should be order=c(0,0,1) instead of order=c(0,1,0) for building the MA model.
I am working through the "Forecasting Using R" DataCamp course. I have completed the entire thing except for the last part of one particular exercise (link here, if you have an account), where I'm totally lost. The error help it's giving me isn't helping either. I'll put the various parts of the task down with the code I'm using to solve them:
Produce time plots of only the daily demand and maximum temperatures with facetting.
autoplot(elec[, c("Demand", "Temperature")], facets = TRUE)
Index elec accordingly to set up the matrix of regressors to include MaxTemp for the maximum temperatures, MaxTempSq which represents the squared value of the maximum temperature, and Workday, in that order.
xreg <- cbind(MaxTemp = elec[, "Temperature"],
MaxTempSq = elec[, "Temperature"] ^2,
Workday = elec[,"Workday"])
Fit a dynamic regression model of the demand column with ARIMA errors and call this fit.
fit <- auto.arima(elec[,"Demand"], xreg = xreg)
If the next day is a working day (indicator is 1) with maximum temperature forecast to be 20°C, what is the forecast demand? Fill out the appropriate values in cbind() for the xreg argument in forecast().
This is where I'm stuck. The sample code they supply looks like this:
forecast(___, xreg = cbind(___, ___, ___))
I have managed to work out that the first blank is fit, so I'm trying code that looks like this:
forecast(fit, xreg = cbind(elec[,"Workday"]==1, elec[, "Temperature"]==20, elec[,"Demand"]))
But that is giving me the error hint "Make sure to forecast the next day using the inputs given in the instructions." Which... doesn't tell me anything useful. Any ideas what I should be doing instead?
When you are forecasting ahead of time, you use new data that was not included in elec (which is the data set you used to fit your model). The new data was given to you in the question (temperature 20C and workday 1). Therefore, you do not need elec in your forecastcall. Just use the new data to forecast ahead:
forecast(fit, xreg = cbind(20, 20^2, 1))
I have been experimenting with a R package called RNN.
The following is the code site:
https://github.com/bquast/rnn
It has a very nice example for financial time series prediction.
I have read the code and I understand it uses the sequence of the time series to predict in advance the value of next day instrument.
The following is an example of run with 10 hidden nodes and 200 epochs
RNN financial time series prediction
What I would expect as result is that the algorithm succeed, at least in part, to predict in advance the value of the instrument.
From what I can see, apparently is only approximating the value of the time series at the current day, not giving any prediction on the next day.
Is my expectation wrong?
This code is very simple, how would you improve it?
y <- X[,1:input$training_amount+input$prediction_gap,as.numeric(input$target)]
matrix(y, ncol=input$training_amount)
y.train moves all the data forward by a day so that is what is being trained on - next day data for the currency pair you care about. With ncol = training_amount when there are too many columns (with them now equal to training_amount + prediction_gap), the first data points fall off; hence all the data gets moved forward by the prediction_gap.
i'm new to R, so I'm having trouble with this time series data
For example (the real data is way larger)
data <- c(7,5,3,2,5,2,4,11,5,4,7,22,5,14,18,20,14,22,23,20,23,16,21,23,42,64,39,34,39,43,49,59,30,15,10,12,4,2,4,6,7)
ts <- ts(data,frequency = 12, start = c(2010,1))
So if I try to decompose the data to adjust it
ts.decompose <- decompose(ts)
ts.adjust <- ts - ts.decompose$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.