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Time series

Source: vignettes/time-series.Rmd
time-series.Rmd

depictr provides a small, consistent set of time-series plots. The examples use the bundled monthly_sales dataset: two product lines (indoor and outdoor), each with six years of monthly observations carrying a trend, a twelve-month seasonal cycle and noise.

Plotting one or several series

timeseries_plot() accepts a ts object, a numeric vector, or a data frame with time, value and (optionally) group columns. Passing the long-form data frame with a grouping column draws both series at once, each in its own colour; a moving-average overlay is one argument away.

timeseries_plot(monthly_sales, time = date, value = sales, group = series,
                rolling = 12, title = "Monthly sales by product line",
                y_lab = "Units")

For the single-series views (decomposition, autocorrelation, the seasonal plot and forecasting) we extract one line as a monthly ts object of frequency 12.

indoor <- subset(monthly_sales, series == "indoor")
indoor <- indoor[order(indoor$date), ]
indoor_ts <- ts(indoor$sales, start = c(2018, 1), frequency = 12)

Decomposition

decompose_plot() separates a seasonal series into trend, seasonal and remainder components. Use method = "stl" (the default, loess-based) or method = "classical". Setting confidence = TRUE shades a band around the smoothed trend, from the spread of the remainder, so the scale of the unexplained variation is visible rather than implied by the line alone.

decompose_plot(indoor_ts, confidence = TRUE, title = "Indoor sales, decomposed")

Classical decomposition instead holds the seasonal component fixed across the whole series, where STL lets it evolve from year to year.

decompose_plot(indoor_ts, method = "classical",
               title = "Indoor sales, classical decomposition")

Autocorrelation

acf_plot() draws the autocorrelation (or, with type = "partial", the partial autocorrelation) function, with approximate significance bounds. The spikes at multiples of twelve are the annual seasonality.

acf_plot(indoor_ts)

acf_plot(indoor_ts, type = "partial")

The seasonal pattern up close

seasonal_plot() draws a seasonal-subseries (cycle) plot: one small panel per month, with the value traced across successive years and a reference line at each month’s mean. This shows the seasonal shape (differences between panels) and the year-on-year trend within each month (the slope inside each panel) at the same time, something a single overlaid line cannot do.

seasonal_plot(indoor_ts, title = "Indoor sales: monthly subseries")

With style = "season" every year becomes its own line over the months on a shared axis, which is handy for spotting an unusual year.

seasonal_plot(indoor_ts, style = "season",
              title = "Indoor sales: one line per year")

Forecasting

ts_forecast() is a lightweight, dependency-free forecaster: it decomposes the series with STL, extrapolates the recent trend, carries the seasonal pattern forward, and returns point forecasts with prediction intervals that widen with the horizon.

fc <- ts_forecast(indoor_ts, h = 18, level = 0.9)
head(fc)
#>       time      fit      lwr      upr
#> 1 2024.000 325.4647 308.9359 341.9935
#> 2 2024.083 344.0097 320.6345 367.3850
#> 3 2024.167 355.7215 327.0927 384.3502
#> 4 2024.250 368.7429 335.6853 401.8005
#> 5 2024.333 361.4311 324.4716 398.3906
#> 6 2024.417 355.8928 315.4057 396.3799

Passing an integer horizon straight to timeseries_plot() overlays that forecast on the history: the point forecast continues the line and the shaded ribbon shows the (growing) 90% prediction interval.

timeseries_plot(indoor_ts, forecast = 18, level = 0.9,
                title = "Indoor sales with an 18-month forecast",
                y_lab = "Units")

For a fully specified statistical model, fit it yourself (for example with forecast::forecast()) and pass the resulting time/fit/lwr/upr columns to timeseries_plot(forecast = ) as a data frame.