A synthetic numeric sensor-reading stream: 500 stable observations centred at 0, then 500 after the sensor drifted out of calibration and the mean shifted to 2. Frozen with a fixed seed.
Format
A tibble with 1,000 rows and 3 columns:
- t
Observation index, 1 to 1000.
- value
Numeric sensor reading.
- drift_true
Ground truth:
TRUEfrom observation 501 on, the point the sensor drifted.
Source
Simulated with sim_dist_stream():
sim_dist_stream(n_pre = 500, n_post = 500, mean_pre = 0, mean_post = 2, seed = 2).
See data-raw/sensor-monitoring.R.
Examples
sensor_monitoring
#> # A tibble: 1,000 × 3
#> t value drift_true
#> <int> <dbl> <lgl>
#> 1 1 -0.897 FALSE
#> 2 2 0.185 FALSE
#> 3 3 1.59 FALSE
#> 4 4 -1.13 FALSE
#> 5 5 -0.0803 FALSE
#> 6 6 0.132 FALSE
#> 7 7 0.708 FALSE
#> 8 8 -0.240 FALSE
#> 9 9 1.98 FALSE
#> 10 10 -0.139 FALSE
#> # ℹ 990 more rows
detect_drift(sensor_monitoring, .col = value, method = "kswin", seed = 7)
#> # A tibble: 1,000 × 5
#> t value drift_true .warning .drift
#> <int> <dbl> <lgl> <lgl> <lgl>
#> 1 1 -0.897 FALSE NA NA
#> 2 2 0.185 FALSE NA NA
#> 3 3 1.59 FALSE NA NA
#> 4 4 -1.13 FALSE NA NA
#> 5 5 -0.0803 FALSE NA NA
#> 6 6 0.132 FALSE NA NA
#> 7 7 0.708 FALSE NA NA
#> 8 8 -0.240 FALSE NA NA
#> 9 9 1.98 FALSE NA NA
#> 10 10 -0.139 FALSE NA NA
#> # ℹ 990 more rows