stsm_detect_anomalies {autostsm} | R Documentation |

Detect anomalies using the estimated structural time series model

```
stsm_detect_anomalies(
model,
y = NULL,
freq = NULL,
exo_obs = NULL,
exo_state = NULL,
sig_level = 0.01,
smooth = TRUE,
plot = FALSE
)
```

`model` |
Structural time series model estimated using stsm_estimate. |

`y` |
Univariate time series of data values. May also be a 2 column data frame containing a date column. |

`freq` |
Frequency of the data (1 (yearly), 4 (quarterly), 12 (monthly), 365.25/7 (weekly), 365.25 (daily)), default is NULL and will be automatically detected |

`exo_obs` |
Matrix of exogenous variables to be used in the observation equation. |

`exo_state` |
Matrix of exogenous variables to be used in the state matrix. |

`sig_level` |
Significance level to determine statistically significant anomalies |

`smooth` |
Whether or not to use the Kalman smoother |

`plot` |
Whether to plot everything |

data table (or list of data tables) containing the dates of detected anomalies from the filtered and/or smoothed series

```
## Not run:
#GDP Not seasonally adjusted
library(autostsm)
data("NA000334Q", package = "autostsm") #From FRED
NA000334Q = data.table(NA000334Q, keep.rownames = TRUE)
colnames(NA000334Q) = c("date", "y")
NA000334Q[, "date" := as.Date(date)]
NA000334Q[, "y" := as.numeric(y)]
NA000334Q = NA000334Q[date >= "1990-01-01", ]
stsm = stsm_estimate(NA000334Q)
anomalies = stsm_detect_anomalies(model = stsm, y = NA000334Q, plot = TRUE)
## End(Not run)
```

[Package *autostsm* version 3.1.2 Index]