I am fairly new to this field and I would like to get some help/advices. Any help would be much appreciated!
I am currently doing a forecasting project with time series data. However, it does not contain any weekend/holiday data. My goal is to predict the future value on a specific date. For example, with given data from 2000-present, I would like to predict the value of 2023-05-01. I tried creating some plots and use the zoo package. However, I am unsure how to approach this unevenly spaced data. Can someone provide me with some ideas of what model I should try? Btw, I am using R for this project. Thank you all so much!
I would agree with #jhoward that this is missing data, not unevenly spaced (like timestamped data). So you can interpolate the missing data. Maybe this helps for an overview of the possible techniques: 4-techniques-to-handle-missing-values-in-time-series-data
Related
I have data that represents about 50,000 different 2-year monthly time series. What would be the most convenient and tidyverse-ish way to store that in R? I'll be using R to review each series, trying to extract characteristic features of their shapes.
Somehow a data frame with 50,000 rows and 24 columns (plus a few more for meta data) seems awkward, because the time axis is in the columns. But what else should I be using? A list of xts objects? A data frame with 50,000x24 rows? A three-dimensional matrix? I'm not really seeing anything obviously convenient, and my friend google hasn't found any great examples for me either. I imagine this means I'm overlooking the obvious solution, so maybe someone can suggest it. Any help?
[Hey, I am sorry if this question might be too easy for this forum! My task was to decompose a time series to break it into the different components. Then plot it in R.
However, the correct solutions included the part that I uploaded as a picture. We are trying to identify the monthly data right? but why are we using the cbind and the data.frame function? than you very much in advance[enter image description here]1 ][2]
I am trying to figure out how to approach a data problem that includes observations of multiple equipment units' pressure and temperature measures. The measures are available for a few years as daily or nearly daily values.
This seems like a time series problem (multivariate) and I have found some quality examples. However, because the data set consists of multiple measures taken for each equipment unit, I am a bit stumped on how to proceed. Should I fit a separate time series for each piece of equipment? This seems intuitively wrong, but I am really not sure which package or even approach I can use to work through this.
I would very much appreciate a recommendation or link to some resources.
I'm trying to understand accurate way of forecasting grouped time series specified as in example posted here. I wanted to use all hierarchical forecasting methods available in hts package with base model - ARIMA. I get an error with some of them (methods: 'mo', 'tdfp', 'tdgsa', 'tdgsf') and as I found on GitHub these methods are not available for gts class object.
I've huge problem with understanding why. As I know both of grouped time series are like 2 hierarchies, so my thought was that we can use for e.g. top-down methods for each hierarchy independently (and the same with middle-out method).
Maybe somebody more familiar with construction of these methods could describe the problem of usage these methods on grouped time series?
Any kind of help would be appreciated.
[ Edit ]
My goal is to analyse forecast accuracy of grouped time series.
To be more detailed I've 2 variables used for disaggregation: car owner (A, B) and car type(X, Y). And as I understood from this paper this data can be describe as groped time series. Am I wrong?
It's really hard for me to find clear definition of grouped time series and examples of forecasting such data. So I would be happy to get links/reference to further literature.
Many thanks for replay!
Two hierarchies is not a hierarchy. If you want to use methods designed for purely hierarchical data, then specify the hierarchy. Do it for the two hierarchies and average the results.
I want to use forecasting to my data and I have used the auto arima method and got graph.
The following is my code,
fit <- auto.arima(a)
LH.pred <- forecast(fit,h=30)
plot(LH.pred)
I want to interpret the graphs as values and store it in a data frame, so that I can make calculations based on the forecasting.
Can anybody let me know how to take the values from the graph and store it in a data frame?
Also when I used the auto arima method, the days just got converted to days count from 1-1-1970. I want to convert back to normal dates. Can anybody plese help in that too?
Thanks
Observer
Taking the values from the graph is not really necessary. The graph consists of two parts. The first one is the time series 'a' used to build 'fit'. It is still stored in 'fit' as 'fit$x'. The second part is the forecast. You can take it from 'LH.pred' using 'as.data.frame(LH.pred)'.