I have a M multivariate time series data, by multivariate I mean that a time series is represented by more than one variable that varies in time (see example image for simulated data). All have the same size. I want to build a classifier trained on K class (eg. all time series data belongs to A, B or C class).
Is there a straightforward implementation of this in R, specifically, as the regular classification approaches (e.g random forest, SVM) will ignore the dependent data and give different predictions within the same time series. I have an intuition how this could be solved, e.g. using some ensemble classification, or concatenating time series into a univariate vector, but I have a feeling there is a better approach for this that doesn't require me to reinvent the wheel. I also know that KNN and DTW approach could in theory work, but not sure how they get around these issues above (e.g. the multivariate problem)
Appreciate any pointers and references
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I would like to create a forecasting model with time series in R. I have a target time series 'Sales' that I would like to forecast. I also have several time series that represent, for example, GDP or advertising spend. Unfortunately I have a lot of independent time series and I don't know how to figure out the most significant ones. It would be best to find out the most important ones already before building the model.
I have already worked with classification problems, here I have always used the Pearson correlation value. This is not possible with time series, right? How can I determine the correlation for time series and use the correlation to find suitable time series that describe my target time series?
I tried to use the corr.test() function in R, but I think thats not right.
I used the forecast package to forecast the daily time-series of variable Y using its lag values and a time series of an external parameter X. I found nnetar model (a NARX model) was the best in terms of overall performance. However, I was not able to get the prediction of peaks of the time series well despite my various attempts with parameter tuning.
I then extracted the peak values (above a threshold) of Y (and of course this is not a regular time series anymore) and corresponding X values and tried to fit a regression model (note: not an autoregression model) using various models in carat package. I found out the prediction of peak values using brnn(Bidirectional recurrent neural networks) model just using X values is better than that of nnetar which uses both lag values and X values.
Now my question is how do I go from here to create ensamples of these two models (i.e whenever the prediction using brnn regression model ( or any other regression model) is better I want to replace the prediction using nnetar and move forward - I am mostly concerned about the peaks)? Is this a commonly used approach?
Instead of trying to pick one model that would be the superior at anytime, it's typically better to do an average of the models, in order to include as many individual views as possible.
In the experiments I've been involved in, where we tried to pick one model that would outperform, based on historical performance, it's typically shown that a simple average was as good or better. Which is in line with the typical results on this problem: https://otexts.com/fpp2/combinations.html
So, before you try to go more advanced at it by using trying to pick a specific model based on previous performance, or by using an weighted average, consider doing a simple average of the two models.
If you want to continue with a sort of selection/weighted averaging, try to have a look at the FFORMA package in R: https://github.com/pmontman/fforma
I've not tried the specific package (yet), but have seen promising results in my test using the original m4metalearning package.
I have time series data ranging from 0 to 30 million. Its basically web traffic weekly data. I am working on building a forecasting model with this data. I want to understand how can I deal with this range of data. I tried box cox transformation with prophet model. I am not sure about what metrics could I use to evaluate the performance of the model. The data has a lot of 0's. I can't remove them from the dataset. Is there a better way to deal with the 0's other than the Box Cox transformation? I had issues with the inverse transformation but I added a small value (0.1) to the data to avoid negative values.
If your series have lot of periodic zero data,Croston method is a one way.It is a basically forecast strategy for products with intermittent demand.Also you can try exponential smoothing and traditional ARIMA,SARIMA models and clip the negative values in the forecast(this is according to your use case).
you can find croston method in forecast package.
also refer these links as well.
https://stats.stackexchange.com/questions/8779/analysis-of-time-series-with-many-zero-values/8782
https://stats.stackexchange.com/questions/373689/forecasting-intermittent-demand-with-zeroes-in-times-series
https://robjhyndman.com/papers/foresight.pdf
I am currently using the Marima package for R invented by Henrik Spliid in order to forecast multivariate time series with ARIMA.
Overview can be found here:
https://cran.r-project.org/web/packages/marima/marima.pdf
http://orbit.dtu.dk/files/123996117/marima.anv.talk.pdf
When using the Marima function, it is required to define both the order of AR(p) and MA(q) first.
My question is, how can I determine appropriate values for p and q?
I know when it comes to univariate ARIMA analysis, that auto.arima gives a good suggestion for p and q. However, when I use auto.arima for every single univariate time series I want to analyze, there are (slightly) different suggestions for each time series. (For example (2,2,1) for the first, (1,1,1) for the second and so on)
Since I want to analyze all of the time series combined in the multivariate ARIMA model and I only can choose one value for each p and q (if I understood it correctly), I wonder how I can choose those values the most accurate way.
Could I just try to run the model a couple times and see what values for p and q work best (e.g. by testing the residuals of the forecast)?
What are your suggestions?
I would appreciate any help!
I am wondering what the good approach for testing time series model would be. Suppose I have a time series in a time domain t1,t2,...tN. I have inputs, say, zt1, zt2,...ztN and output x1,x2...xN.
Now, if that were a classical data mining problem, I could go with known approaches like cross-validation, leave-one-out, 70-30 or something else.
But how should I approach the problem of testing my model with time series? Should I build the model on the first t1,t2,...t(N-k) inputs and test it on the last k inputs? But what if we want to maximise the prediction for p steps ahead and not k (where p < k). I am looking for a robust solution which I can apply to my specific case.
With timeseries fitting, you need to be careful about not using your Out-of-sample data until after you've developed your model. The main problem with modelling is that it's simply easy to overfit.
Typically what we do is to use 70% for in-sample modelling, 30% of out-of-sample testing/validation. And when we use the model in production, the data we collect day-to-day becomes true-out-of-sample data : the data you have never seen or used.
Now, if you have enough data points, I'd suggest trying rolling window fitting approach. For each time step in your in-sample, you look back N time steps to fit your model and see how the parameters in your model varies over time. For example, let's say your model is linear regression with Y = B0 + B1*X1 + B2*X2. You'd do regression N - window_size time over the sample. This way, you understand how sensitive your Betas are in relation to time, among other things.
It sounds like you have a choice between
Using the first few years of data to create the model, then seeing how well it predicts the remaining years.
Using all the years of data for some subset of input conditions, then seeing how well it predicts using the remaining input conditions.