Is there a way to compare ARIMA models across different variables? I noticed you had 5 variables in your dataframe but when you create the time series object you only select one of the columns. I have hourly temperature data I would like to compare across sites. Is ARIMA still the right way to go?
Thanks! It depends a bit on what kind of model you're interested in using. Both the ARIMA based models illustrated here and the exponential smoothing models in forecast will take an xreg argument for drivers that will be modeled linearly. If you want to build what I'd call a time-series linear model then that's available in the next generation package that is slated to replace forecast, fable (fable.tidyverts.org/reference/TSLM.html). Fable also lets you do the same things with ARIMA & ETS as forecast (as well as some more complex approaches), so it probably has what you need fable.tidyverts.org/index.html
You're welcome! Glad it helped! I don't work with the dygraph package frequently, but here's the basic idea based on my understanding. dygraph takes a group of time-series as input. In this case if you want to plot the predictions (like shown in the Predicted Lunch Deaths example on rstudio.github.io/dygraphs/) then we just need to get them in the right format. To make this simpler let's work with a version of the seasonal Arima that only has 1 set of prediction intervals: # rerun the forecast with just a single prediction internal seasonal_arima_forecast = seasonal_arima_model, h = 36, level = 80) # store the data as a group of time-series forecasts_for_dygraph = cbind(lower = seasonal_arima_forecast$lower, mean = seaonal_arima_forecast$mean, upper = seasonal_arima_forecast$upper) # make a dygraph following the instructions in the example dygraph(forecasts_for_dygraph) %>% dySeries(c("lower", "mean", "upper") Hope that helps!
Thanks for tthis lecture, i kindly want to ask, is there are larger part of this dataset, i want to explore more approaches from all your lecture series..... i mean like, where cam i get the original dataset
You’re welcome! The full dataset is available here: zenodo.org/doi/10.5281/zenodo.1215988 There is a R package designed to make it easy to download and get the pieces of the data you want in useful formats as well: weecology.github.io/portalr/ Let us know if there is anything else we can do to help as you explore the data. You are welcome to email us at portal@weecology.org
Thanks! The (0, 0, 0) there is setting the model parameters/structure. Since they are all zeros (no autoregressive component, no integration, no moving average component) the model then just becomes the mean value with normally distributed error. So we're just starting with the simplest model possible. If you're interested in learning more about what those three components of ARIMA models are checkout these two videos: * ru-vid.com/video/%D0%B2%D0%B8%D0%B4%D0%B5%D0%BE-hD13nv8SK6A.html * ru-vid.com/video/%D0%B2%D0%B8%D0%B4%D0%B5%D0%BE-6gmCNGRrRBs.html
@weecology gives a nice answer, but to illustrate, even further suppose you use ARIMA(p,0,q) (i.e. there are no differencing parameters), which is basically an ARMA(p,q): the theoretical formula of ARMA(p,q,) is: yt = constant + sum of p lags of past y values + sum of q lags of past error terms When p = q = 0, yt = constant (i.e. your average)
Hi Is there any chance you can check my assignment report on Forecasting in Business and Economics on time Series and analysis. Just corrections and see if it meets the objectives of the report. Thanks
Is it possible to make rstudio produce a table vs a plot graph to display the forecasted numbers? If I want to forecast 2023 sales, how can I see the values arima is producing in the plot graph?
Definitely. The data for the point forecast is stored in the $mean object, which you can access using your_forecast$mean. The video shows how to extract this information at around 11 minutes. You can also extract the upper and lower prediction intervals using $upper and $lower as shown in ru-vid.com/video/%D0%B2%D0%B8%D0%B4%D0%B5%D0%BE-hGlnIVYFUgg.html.
It depends on how the data is structure in the .txt file. If it's comma delimited you can do it the exact same way. If there is a different delimiter the you specify that delimiter using the optional `sep` argument in `read.csv`
NDVI is a remotely sensed measure of greenness that captures how much vegetation there is. See en.wikipedia.org/wiki/Normalized_difference_vegetation_index
@@user-jg2py7jj8c You're welcome! If you're interested in this stuff we have significantly expanded written tutorials using the newer fable R package. Here's the key lessons: * course.naturecast.org/lessons/r-time-series-modeling/ * course.naturecast.org/lessons/r-time-series-modeling-2/ * course.naturecast.org/lessons/r-time-series-modeling-3/
@@researchmadeeasybydr.tanvi7663 OK, thanks. If we look at the model description is it ARIMA(0, 1, ). Assuming that you fit this with `auto.arima()`, this tells us that model that the best fitting model has no autoregressive component (the first zero), a first order difference (the 1 in the middle), and no moving average component (the last zero). This means that there is a trend, but once it is accounted for there is no meaningful autocorrelation in the time-series. As a result, your forecast is a directional trend with no wiggles in it. Does that help?
Source code for this lesson is available in the text version of the tutorial here: course.naturecast.org/lessons/r-intro-to-forecasting/r_tutorial/, but note that it's been updated to use the newer fable package. The text version of this tutorial is available in our GitHub history here: github.com/weecology/forecasting-course/blob/2e7be6bc4f0aeb265ab55836cdf535f4a863d6c9/content/lessons/R-intro-to-forecasting/r_tutorial.md In general you can find text versions of all tutorials at course.naturecast.org/
