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docs/vmanomaly - release 1.18.2 (#7533)
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### Describe Your Changes docs update for `vmanomaly` release 1.18.2 ### Checklist The following checks are **mandatory**: - [x] My change adheres [VictoriaMetrics contributing guidelines](https://docs.victoriametrics.com/contributing/).
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@ -72,7 +72,7 @@ services:
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restart: always
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vmanomaly:
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container_name: vmanomaly
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image: victoriametrics/vmanomaly:v1.18.1
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image: victoriametrics/vmanomaly:v1.18.2
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depends_on:
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- "victoriametrics"
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ports:
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@ -11,6 +11,11 @@ aliases:
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---
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Please find the changelog for VictoriaMetrics Anomaly Detection below.
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## v1.18.2
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Released: 2024-11-13
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- IMPROVEMENT: Enhanced the flexibility of the [`ProphetModel`](https://docs.victoriametrics.com/anomaly-detection/components/models/#prophet) for tz-aware data (`tz_aware = True`). The `tz_seasonalities` argument has been reformatted to align with the structure of the existing `seasonalities` argument. For more details, refer to the [model section here](https://docs.victoriametrics.com/anomaly-detection/components/models/#prophet). Additionally, tz-aware support for `ProphetModel` has been added to [`AutoTuned`](https://docs.victoriametrics.com/anomaly-detection/components/models/#autotuned) model wrapper. This feature is automatically enabled if the data is timezone-aware and its timezone is not set to the default ('UTC'), otherwise default timezone-free optimization flow will be used.
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## v1.18.1
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Released: 2024-11-12
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@ -159,7 +159,7 @@ services:
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# ...
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vmanomaly:
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container_name: vmanomaly
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image: victoriametrics/vmanomaly:v1.18.1
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image: victoriametrics/vmanomaly:v1.18.2
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# ...
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ports:
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- "8490:8490"
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@ -229,7 +229,7 @@ This will expose metrics at `http://0.0.0.0:8080/metrics` page.
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To use *vmanomaly* you need to pull docker image:
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```sh
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docker pull victoriametrics/vmanomaly:v1.18.1
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docker pull victoriametrics/vmanomaly:v1.18.2
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```
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> Note: please check what is latest release in [CHANGELOG](https://docs.victoriametrics.com/anomaly-detection/changelog/)
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@ -239,7 +239,7 @@ docker pull victoriametrics/vmanomaly:v1.18.1
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You can put a tag on it for your convenience:
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```sh
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docker image tag victoriametrics/vmanomaly:v1.18.1 vmanomaly
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docker image tag victoriametrics/vmanomaly:v1.18.2 vmanomaly
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```
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Here is an example of how to run *vmanomaly* docker container with [license file](#licensing):
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@ -58,13 +58,13 @@ Below are the steps to get `vmanomaly` up and running inside a Docker container:
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1. Pull Docker image:
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```sh
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docker pull victoriametrics/vmanomaly:v1.18.1
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docker pull victoriametrics/vmanomaly:v1.18.2
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```
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2. (Optional step) tag the `vmanomaly` Docker image:
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```sh
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docker image tag victoriametrics/vmanomaly:v1.18.1 vmanomaly
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docker image tag victoriametrics/vmanomaly:v1.18.2 vmanomaly
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```
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3. Start the `vmanomaly` Docker container with a *license file*, use the command below.
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@ -98,7 +98,7 @@ docker run -it --user 1000:1000 \
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services:
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# ...
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vmanomaly:
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image: victoriametrics/vmanomaly:v1.18.1
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image: victoriametrics/vmanomaly:v1.18.2
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volumes:
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$YOUR_LICENSE_FILE_PATH:/license
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$YOUR_CONFIG_FILE_PATH:/config.yml
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@ -456,12 +456,15 @@ models:
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> **Note**: `ProphetModel` is a [univariate](#univariate-models), [non-rolling](#non-rolling-models), [offline](#offline-models) model.
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> **Note**: Starting with [v1.18.2](https://docs.victoriametrics.com/anomaly-detection/changelog/#v1182), the format for `tz_seasonalities` has been updated to enhance flexibility. Previously, it accepted a list of strings (e.g., `['hod', 'minute']`). Now, it follows the same structure as custom seasonalities defined in the `seasonalities` argument (e.g., `{"name": "hod", "fourier_order": 5, "mode": "additive"}`). This change is backward-compatible, so older configurations will be automatically converted to the new format using default values.
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*Parameters specific for vmanomaly*:
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* `class` (string) - model class name `"model.prophet.ProphetModel"` (or `prophet` starting from [v1.13.0](https://docs.victoriametrics.com/anomaly-detection/changelog/#1130) with class alias support)
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* `seasonalities` (list[dict], optional): Additional seasonal components to include in Prophet. See Prophet’s [`add_seasonality()`](https://facebook.github.io/prophet/docs/seasonality,_holiday_effects,_and_regressors.html#modeling-holidays-and-special-events:~:text=modeling%20the%20cycle-,Specifying,-Custom%20Seasonalities) documentation for details.
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- `tz_aware` (bool): (Available since [v1.18.0](https://docs.victoriametrics.com/anomaly-detection/changelog/#v1180)) Enables handling of timezone-aware timestamps. Default is `False`. Should be used with `tz_seasonalities` and `tz_use_cyclical_encoding` parameters.
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- `tz_seasonalities` (list[str]): (Available since [v1.18.0](https://docs.victoriametrics.com/anomaly-detection/changelog/#v1180)) Specifies timezone-aware seasonal components. Requires `tz_aware=True`. Supported options include `minute`, `hod` (hour of the day), `dow` (day of the week), and `month` (month of the year).
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- `tz_seasonalities` (list[dict]): (Available since [v1.18.0](https://docs.victoriametrics.com/anomaly-detection/changelog/#v1180)) Specifies timezone-aware seasonal components. Requires `tz_aware=True`. Supported options include `minute`, `hod` (hour of day), `dow` (day of week), and `month` (month of year). Starting with [v1.18.2](https://docs.victoriametrics.com/anomaly-detection/changelog/#v1182), users can configure additional parameters for each seasonality, such as `fourier_order`, `prior_scale`, and `mode`. For more details, please refer to the **Timezone-unaware** configuration example below.
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- `tz_use_cyclical_encoding` (bool): (Available since [v1.18.0](https://docs.victoriametrics.com/anomaly-detection/changelog/#v1180)) If set to `True`, applies [cyclical encoding technique](https://www.kaggle.com/code/avanwyk/encoding-cyclical-features-for-deep-learning) to timezone-aware seasonalities. Should be used with `tz_aware=True` and `tz_seasonalities`.
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> **Note**: Apart from standard [`vmanomaly` output](#vmanomaly-output), Prophet model can provide additional metrics.
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@ -489,6 +492,7 @@ models:
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- name: 'hourly'
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period: 0.04166666666
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fourier_order: 30
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prior_scale: 20
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# inner model args (key-value pairs) accepted by
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# https://facebook.github.io/prophet/docs/quick_start.html#python-api
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args:
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@ -504,8 +508,13 @@ models:
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class: 'prophet' # or 'model.prophet.ProphetModel' until v1.13.0
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provide_series: ['anomaly_score', 'yhat', 'yhat_lower', 'yhat_upper', 'trend']
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tz_aware: True
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tz_seasonalities: ['hod', 'dow'] # intra-day + intra-week seasonality, no intra-year / sub-hour seasonality
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tz_use_cyclical_encoding: False
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tz_use_cyclical_encoding: True
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tz_seasonalities: # intra-day + intra-week seasonality, no intra-year / sub-hour seasonality
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- name: 'hod' # intra-day seasonality, hour of the day
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fourier_order: 5 # keep it 3-8 based on intraday pattern complexity
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prior_scale: 10
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- name: 'dow' # intra-week seasonality, time of the week
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fourier_order: 2 # keep it 2-4, as dependencies are learned separately for each weekday
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# inner model args (key-value pairs) accepted by
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# https://facebook.github.io/prophet/docs/quick_start.html#python-api
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args:
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@ -984,7 +993,7 @@ monitoring:
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Let's pull the docker image for `vmanomaly`:
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```sh
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docker pull victoriametrics/vmanomaly:v1.18.1
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docker pull victoriametrics/vmanomaly:v1.18.2
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```
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Now we can run the docker container putting as volumes both config and model file:
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@ -998,7 +1007,7 @@ docker run -it \
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-v $(PWD)/license:/license \
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-v $(PWD)/custom_model.py:/vmanomaly/model/custom.py \
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-v $(PWD)/custom.yaml:/config.yaml \
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victoriametrics/vmanomaly:v1.18.1 /config.yaml \
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victoriametrics/vmanomaly:v1.18.2 /config.yaml \
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--licenseFile=/license
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```
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restart: always
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vmanomaly:
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container_name: vmanomaly
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image: victoriametrics/vmanomaly:v1.18.1
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image: victoriametrics/vmanomaly:v1.18.2
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depends_on:
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- "victoriametrics"
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ports:
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