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docs/vmanomaly - changelog update to v1.15.3 (#6808)

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### Describe Your Changes

changelog updates to v1.15.3 patch of `vmanomaly`

### Checklist

The following checks are **mandatory**:

- [ ] My change adheres [VictoriaMetrics contributing
guidelines](https://docs.victoriametrics.com/contributing/).
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docs/anomaly-detection/CHANGELOG.md
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@ -13,6 +13,10 @@ Please find the changelog for VictoriaMetrics Anomaly Detection below.
> **Important note: Users are strongly encouraged to upgrade to `vmanomaly` [v1.9.2](https://hub.docker.com/repository/docker/victoriametrics/vmanomaly/tags?page=1&ordering=name) or newer for optimal performance and accuracy. <br><br> This recommendation is crucial for configurations with a low `infer_every` parameter [in your scheduler](./components/scheduler.md#parameters-1), and in scenarios where data exhibits significant high-order seasonality patterns (such as hourly or daily cycles). Previous versions from v1.5.1 to v1.8.0 were identified to contain a critical issue impacting model training, where models were inadvertently trained on limited data subsets, leading to suboptimal fits, affecting the accuracy of anomaly detection. <br><br> Upgrading to v1.9.2 addresses this issue, ensuring proper model training and enhanced reliability. For users utilizing Helm charts, it is recommended to upgrade to version [1.0.0](https://github.com/VictoriaMetrics/helm-charts/blob/master/charts/victoria-metrics-anomaly/CHANGELOG.md#100) or newer.**
## v1.15.3
Released: 2024-08-14
- IMPROVEMENT: better config handling of `reader` [section](https://docs.victoriametrics.com/anomaly-detection/components/reader/#vm-reader) if using `vmanomaly` with [helm charts](https://github.com/VictoriaMetrics/helm-charts/tree/master/charts/victoria-metrics-anomaly).
## v1.15.2
Released: 2024-08-13
- IMPROVEMENT: Enhanced [online models](https://docs.victoriametrics.com/anomaly-detection/components/models/#online-models) (e.g., [`OnlineQuantileModel`](https://docs.victoriametrics.com/anomaly-detection/components/models/#online-seasonal-quantile)) to automatically create model instances for unseen time series during `infer` calls, eliminating the need to wait for the next `fit` call. This ensures no inferences are skipped **when using online models**.