[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]
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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
I'm trying to plot some measurements over time with a best-fit curve, using Julia's plotting tools. There's already a fairly good answer how to do this with a simple numeric data series:
Julia: How to find best fit curve / equation when I have a plot?
However, these tools (well - at least the Polynomials package) do not like being given Date values. I imagine some sort of conversion from Dates into a numeric value needs to be done, but I want the resulting plots to retain their scales in date units, which would need a conversion back again.
I suspect this is a fairly common problem and there must be an elegant way to solve it, but being new to Julia I would like to ask for some pointers to what a good solution looks like?
Thanks!
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)'.
I currently try to move from matlab to R.
I have 2D measurements, consisting of irradiance in time and wavelength together with quality flags and uncertainty and error estimates.
In Matlab I extended the timeseries object to store both the wavelength array and the auxiliary data.
What is the best way in R to store this data?
Ideally I would like this data to be stored together such that e.g. window(...) keeps all data synchronized.
So far I had a look at the different timeseries classes like ts, zoo etc and some spatial-time series. However none of them allow me to neither attach auxiliary data to observations nor can they give me a secondary axes.
Not totally sure what you want, but here is a simple tutorial mentioning
R's "ts" and"zoo" time series classes:
http://faculty.washington.edu/ezivot/econ424/Working%20with%20Time%20Series%20Data%20in%20R.pdf
and here is a more comprehensive outline of many more classes(see the Time Series Classes section)
http://cran.r-project.org/web/views/TimeSeries.html
This question already has an answer here:
How can I produce plots like this?
(1 answer)
Closed 9 years ago.
Just read the "Mining time series data" pdf by Ratanamahatana, Lin, Gunopulos and Keogh. Did someone know how to visualize time series clusters in R like in the Figure 1.7?
You can visualize 100s of Time Series sequences with Sparklines. If you also want to the Hierarchical ordering, the you could attain that in 2 steps.
Sort your data.frame of Times Series sequences by their multi-level clusters. (This assumes that you have computed the cluster hierarchy for each series.)
Download and install the SparkTable in your R setup. Now plot the Sparklines for your TS sequences. Take a look at this Inside-R page for SparkEPS.
This answer on statExchange is exactly what you need for the plotting part, so I am not reproducing the same example here.
Hope that helps.
This figure most likely is made with a drawing program, not with a data mining software.
Nobody would run cluster analysis on 6 observations like this. It's easier to look at them visually and do it manually than figuring out how to have a program visualize it this way.