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Fixed bug related to anomaly detection system

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Marco Cetica
2025-10-29 16:33:34 +01:00
parent 9fec72251d
commit f44d3abb23
3 changed files with 28 additions and 23 deletions
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README.md
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@@ -19,12 +19,11 @@ Zephyr is designed to be simple, fast and efficient, providing only the
weather data of a given location, without any additional nonsense.
This service communicates through a JSON API, making it suitable for
any kind of internet-based project or device. I already use it on a widget
on my phone, on my terminal, on the tmux's status bar, in a couple of
smart bedside clocks I've built and as a standalone web app.
any kind of internet-based project or device. I already use it as a [standalone web app](https://m.marcocetica.com),
as a phone widget and on my terminal.
## Weather
As state before, Zephyr talks via HTTP using the JSON format. Therefore, you
As stated before, Zephyr talks via HTTP using JSON formatting. Therefore, you
can query it using any HTTP client of your choice. Below you can find some examples
using `curl`:
@@ -130,7 +129,7 @@ As in the previous example, you can append the `i` query parameter to get result
in imperial units.
## Wind
The `/wind/:city` endpoint provides wind related information(such as speed and direction) for a given city:
The `/wind/:city` endpoint provides wind related information (such as speed and direction) for a given city:
```sh
curl -s 'http://127.0.0.1:3000/wind/bolzano' | jq
@@ -238,7 +237,7 @@ curl -s 'http://127.0.0.1:3000/forecast/tapei?h' | jq
```
As in the previous examples, you can append the `i` query parameter to get results
in imperial units(**tip**: you can mix both parameter using `&`).
in imperial units (**tip**: you can mix both parameter using `&`).
## Moon
@@ -329,7 +328,7 @@ will be flagged as outliers by the model and will be reported as such:
The anomaly detection algorithm is based on a modified version of the
[Z-Score](https://en.wikipedia.org/wiki/Standard_score) algorithm, which uses the
[Median Absolute Deviation](https://en.wikipedia.org/wiki/Median_absolute_deviation) to measure the variability
in a given sample of quantitative data. The algorithm can be summarized as follows(let $X$ be the sample):
in a given sample of quantitative data. The algorithm can be summarized as follows (let $X$ be the sample):
$$
@@ -374,9 +373,9 @@ but no less than a week, *should* follow a normal distribution.
> [!IMPORTANT]
> The anomaly detection algorithm works under the assumption that the weather data
> is normally distributed(at least roughly), this might not be the case on datasets
> with a very small number of samples(e.g. few days of data) or with a large
> number of samples(e.g. multi-seasonal data).
> is normally distributed (at least roughly), this might not be the case on datasets
> with a very small number of samples (e.g. few days of data) or with a large
> number of samples (e.g. multi-seasonal data).
The algorithm works quite well when these conditions are met, and even with real world data,
the results were quite satisfactory. However, if it
@@ -399,12 +398,12 @@ if you are using their free tier.
## Configuration
Zephyr requires the following environment variables to be set:
| Variable | Meaning |
|----------------------|----------------------------------------|
| `ZEPHYR_ADDR` | Listen address |
| `ZEPHYR_PORT` | Listen port |
| `ZEPHYR_TOKEN` | OpenWeatherMap API key |
| `ZEPHYR_CACHE_TTL` | Cache time-to-live(expressed in hours) |
| Variable | Meaning |
|----------------------|---------------------------------------- |
| `ZEPHYR_ADDR` | Listen address |
| `ZEPHYR_PORT` | Listen port |
| `ZEPHYR_TOKEN` | OpenWeatherMap API key |
| `ZEPHYR_CACHE_TTL` | Cache time-to-live (expressed in hours) |
Each value must be set _before_ launching the application. If you plan to deploy Zephyr using
Docker, you can specify these variables in the `compose.yml` file.