# Anomaly Detection

In this notebook, we’ll implement anomaly detection in time series data

PrerequesitesThis tutorial assumes basic familiarity with StatsForecast. For a minimal example visit the Quick Start

## Introduction

Anomaly detection is a crucial task in time series forecasting. It involves identifying unusual observations that don’t follow the expected dataset patterns. Anomalies, also known as outliers, can be caused by a variety of factors, such as errors in the data collection process, sudden changes in the underlying patterns of the data, or unexpected events. They can pose problems for many forecasting models since they can distort trends, seasonal patterns, or autocorrelation estimates. As a result, anomalies can have a significant impact on the accuracy of the forecasts, and for this reason, it is essential to be able to identify them. Furthermore, anomaly detection has many applications across different industries, such as detecting fraud in financial data, monitoring the performance of online services, or identifying usual patterns in energy usage.

By the end of this tutorial, you’ll have a good understanding of how to detect anomalies in time series data using StatsForecast’s probabilistic models.

**Outline:**

- Install libraries
- Load and explore data
- Train model
- Recover insample forecasts and identify anomalies

ImportantOnce an anomaly has been identified, we must decide what to do with it. For example, we could remove it or replace it with another value. The correct course of action is context-dependent and beyond this notebook’s scope. Removing an anomaly will likely improve the accuracy of the forecast, but it can also underestimate the amount of randomness in the data.

TipYou can use Colab to run this Notebook interactively

## Install libraries

We assume that you have StatsForecast already installed. If not, check this guide for instructions on how to install StatsForecast

Install the necessary packages using `pip install statsforecast`

## Load and explore the data

For this example, we’ll use the hourly dataset of the M4 Competition.

unique_id | ds | y | |
---|---|---|---|

0 | H1 | 1 | 605.0 |

1 | H1 | 2 | 586.0 |

2 | H1 | 3 | 586.0 |

3 | H1 | 4 | 559.0 |

4 | H1 | 5 | 511.0 |

The input to StatsForecast is always a data frame in long
format with
three columns: `unique_id`

, `df`

and `y`

.

`unique_id`

: (string, int or category) A unique identifier for the series.`ds`

: (timestamp or int) A timestamp in format YYYY-MM-DD or YYYY-MM-DD HH:MM:SS or an integer indexing time.`y`

: (numeric) The measurement we wish to forecast.

From this dataset, we’ll select the first 8 time series to reduce the
total execution time. You can select any number you want by changing the
value of `n_series`

.

We can plot these series using the `plot_series`

function from the
`utilsforecast`

package. This function has multiple parameters, and the
required ones to generate the plots in this notebook are explained
below.

`df`

: A pandas dataframe with columns [unique_id, ds, y].`forecasts_df`

: A pandas dataframe with columns [unique_id, ds] and models.`ids`

: A list with the ids of the time series we want to plot.`level`

: Prediction interval levels to plot.`plot_anomalies`

: Whether or not to include the anomalies for each prediction interval.

## Train model

To generate the forecast, we’ll use the
MSTL
model, which is well-suited for low-frequency data like the one used
here. We first need to import it from `statsforecast.models`

and then we
need to instantiate it. Since we’re using hourly data, we have two
seasonal periods: one every 24 hours (hourly) and one every 24*7 hours
(daily). Hence, we need to set `season_length = [24, 24*7]`

.

To instantiate a new StatsForecast object, we need the following parameters:

`models`

: The list of models defined in the previous step.`freq`

: A string or integer indicating the frequency of the data. See pandas’ available frequencies.`n_jobs`

: An integer that indicates the number of jobs used in parallel processing. Use -1 to select all cores.

We’ll now predict the next 48 hours. To do this, we’ll use the
`forecast`

method, which requieres the following arguments:

`df`

: The dataframe with the training data.`h`

: The forecasting horizon.`level`

: The confidence levels of the prediction intervals.`fitted`

: Return insample predictions.

It is important that we select a `level`

and set `fitted=True`

since
we’ll need the insample forecasts and their prediction intervals to
detect the anomalies.

unique_id | ds | MSTL | MSTL-lo-99 | MSTL-hi-99 | |
---|---|---|---|---|---|

0 | H1 | 749 | 607.607223 | 587.173250 | 628.041196 |

1 | H1 | 750 | 552.364253 | 521.069710 | 583.658796 |

2 | H1 | 751 | 506.785334 | 465.894977 | 547.675691 |

3 | H1 | 752 | 472.906141 | 423.114088 | 522.698195 |

4 | H1 | 753 | 452.240231 | 394.064394 | 510.416067 |

We can plot the forecasts using the `plot_series`

function from before.

## Recover insample forecasts and identify anomalies

In this example, an **anomaly** will be any observation outside the
prediction interval of the insample forecasts for a given confidence
level (here we selected 99%). Hence, we first need to recover the
insample forecasts using the `forecast_fitted_values`

method.

unique_id | ds | y | MSTL | MSTL-lo-99 | MSTL-hi-99 | |
---|---|---|---|---|---|---|

0 | H1 | 1 | 605.0 | 605.098607 | 584.678408 | 625.518805 |

1 | H1 | 2 | 586.0 | 588.496673 | 568.076474 | 608.916872 |

2 | H1 | 3 | 586.0 | 585.586856 | 565.166657 | 606.007054 |

3 | H1 | 4 | 559.0 | 554.012377 | 533.592178 | 574.432576 |

4 | H1 | 5 | 511.0 | 510.153508 | 489.733309 | 530.573707 |

We can now find all the observations above or below the 99% prediction interval for the insample forecasts.

unique_id | ds | y | MSTL | MSTL-lo-99 | MSTL-hi-99 | |
---|---|---|---|---|---|---|

0 | H1 | 1 | 605.0 | 605.098607 | 584.678408 | 625.518805 |

1 | H1 | 2 | 586.0 | 588.496673 | 568.076474 | 608.916872 |

2 | H1 | 3 | 586.0 | 585.586856 | 565.166657 | 606.007054 |

3 | H1 | 4 | 559.0 | 554.012377 | 533.592178 | 574.432576 |

4 | H1 | 5 | 511.0 | 510.153508 | 489.733309 | 530.573707 |

We can plot the anomalies by setting the `level`

and the
`plot_anomalies`

arguments of the `plot_series`

function.

If we want to take a closer look, we can use the `ids`

argument to
select one particular time series, for example, `H10`

.

Here we identified the anomalies in the data using the MSTL model, but any probabilistic model from StatsForecast can be used. We also selected the 99% prediction interval of the insample forecasts, but other confidence levels can be used as well.