Anomaly-Detection-using-AutoEncoders | Time Series Anomaly Detection using LSTM Autoencoders | Time Series Database library

You need to use the persistent volume claim that you have created instead of emptyDir in your deployment:

It appears to be a typo in the configuration file. As stated in the documentation, the configuration file should hold http-bind-address instead of bind-address. As well as a locked port by the first configuration.

The first few lines of the file /etc/influxdb/influxdb.conf should look like so:

Take a look at vmagent. It can be installed at every device where metrics from local sensors must be collected (e.g. at the edge), and collect all these metrics via various popular data ingestion protocols. Then it can push the collected metrics to a centralized time series database such as VictoriaMetrics. Vmagent buffers the collected metrics on the local storage when the connection to a centralized database is unavailable, and pushes the buffered data to the database as soon as the connection is recovered. Vmagent works on Rasberry PI and on any device with ARM, ARM64 or AMD64 architecture.

See use cases for vmagent for more details.

The first approach is better from the performance and disk space usage PoV. InfluxDB stores each field in a separate column. If a column contains similar numeric values, then it may be compressed better compared to the column with JSON strings. This also improves query speed when selecting only a subset of fields or filtering on a subset of fields.

P.S. InfluxDB may need high amounts of RAM for big number of fields and big number of tag combinations (aka high cardinality). In this case there are alternative solutions, which support InfluxDB line protocol and require lower amounts of RAM for high cardinality time series. See, for example, VictoriaMetrics.

The best practice is to capture the failures in their own time series for separate analysis.

Failures or bad readings will skew the series, so they should be filtered out or replaced with a projected value for 'normal' events. The beauty of a time series is that one measure (time) is globally common, so it is easy to project between two known points when one is missing.

The failure information is also important, as it is an early indicator to issues or outages on your target. You can record the network error and other diagnostic information to find trends and ensure it is the client and not your server having the issue. Further, there can be several instances deployed to monitor the same target so that they cancel each other's noise.

You can also monitor a known endpoint like google's 204 page to ensure network connectivity. If all the monitors report an error connecting to your site but not to the known endpoint, your server is indeed down.

The solution I found is to expand the date range using the expand_limits() function in ggplot2 so that some days in May are included. By padding the range, I get the correct output

I suspect you're going to be most interested in timechart for this

Something along the following lines may get you towards what you're looking for:

This can be done in a few shorts steps on Compute Engine. When creating a new instance, choose the region and instance type, then:

• In the "Container" section, enable "Deploy a container image to this VM instance"
• type questdb/questdb:latest for the "Container image"

This will pull the latest QuestDB docker image and run it on your instance when launching. The rest of the setup steps are setting firewall rules to allow networking on the ports you require:

• port 9000 - web console & REST API
• port 8812 - PostgreSQL wire protocol

Source of this info is an ETL tutorial by Gabor Boros which deploys QuestDB to GCP and uses Cloud Functions for loading and processing data from a storage bucket.

You want to add nine hours to midnight: