For Christmas I’ll travel to Marseille. What temperatures should I expect there? I could of course open a weather app, but in this vignette I want to give an example using the
The name of the network for France is "FR__ASOS". I already know there’s only one airport near the city.
## # A tibble: 1 x 4 ## id name lon lat ## <chr> <chr> <dbl> <dbl> ## 1 LFML Marseille 5.23 43.4
We’ll transform it to daily average, and convert Fahrenheit to Celsius thanks to the
weathermetrics package. We impute the missing values and remove outliers via the use of
marseille <- riem_measures(station = marseilles_airport$id, date_start = "2010 01 01") marseille <- group_by(marseille, day = as.Date(valid)) marseille <- summarize(marseille, temperature = mean(tmpf)) marseille <- mutate(marseille, temperature = weathermetrics::fahrenheit.to.celsius(temperature)) library("ggplot2") library("forecast") marseille_ts = ts(as.vector(tsclean(marseille$temperature)), freq=365.25, start=c(2010, 1)) autoplot(marseille_ts) + ylab("Daily average temperature in Marseille airport (ºC)") + xlab("Time (days)")
For this we use the
forecast package. We use the
stlm because our time series obviously present yearly seasonality.
fit <- stlm(marseille_ts) pred <- forecast(fit, h = 7) # plot theme_set(theme_gray(base_size = 14)) autoplot(pred) + ylab("Daily average temperature in Marseille airport (ºC)") + xlab("Time (days)") + ggtitle("How cold will I be during the holidays?", subtitle = "Data accessed via the rOpenSci riem package and forecasted with forecast")
Mmh I don’t see anything, but
autoplot.forecast has an
include parameters, so I’ll only plot the last 31 values.
Ok, what if I had travelled to, say, Hyderabad in India?
## `summarise()` ungrouping output (override with `.groups` argument)
Without surprise, we forecast I’d have enjoyed warmer weather.
I wouldn’t advise you to really use such code to forecast temperature, but I’d recommend you to use
riem for getting weather airport data quite easily and to dig more deeply into
forecast functionalities if you’re interested in time series forecasting. And stay warm!