timemachines | Predict time-series with one line of code | Time Series Database library

 by   microprediction Python Version: 0.20.5 License: MIT

kandi X-RAY | timemachines Summary

kandi X-RAY | timemachines Summary

timemachines is a Python library typically used in Database, Time Series Database applications. timemachines has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can install using 'pip install timemachines' or download it from GitHub, PyPI.

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              timemachines has a low active ecosystem.
              It has 360 star(s) with 49 fork(s). There are 9 watchers for this library.
              OutlinedDot
              It had no major release in the last 12 months.
              There are 78 open issues and 26 have been closed. On average issues are closed in 64 days. There are no pull requests.
              It has a neutral sentiment in the developer community.
              The latest version of timemachines is 0.20.5

            kandi-Quality Quality

              timemachines has 0 bugs and 0 code smells.

            kandi-Security Security

              timemachines has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              timemachines code analysis shows 0 unresolved vulnerabilities.
              There are 0 security hotspots that need review.

            kandi-License License

              timemachines is licensed under the MIT License. This license is Permissive.
              Permissive licenses have the least restrictions, and you can use them in most projects.

            kandi-Reuse Reuse

              timemachines releases are available to install and integrate.
              Deployable package is available in PyPI.
              Build file is available. You can build the component from source.
              Installation instructions are not available. Examples and code snippets are available.

            Top functions reviewed by kandi - BETA

            kandi has reviewed timemachines and discovered the below as its top functions. This is intended to give you an instant insight into timemachines implemented functionality, and help decide if they suit your requirements.
            • Wrapper for sklearn . sklearn
            • Factory function for batch_skater_factory .
            • Update elo with sklearn .
            • Create an ensemble .
            • Factory function for the residuals .
            • This function returns a list of house numbers .
            • Simple smdk example .
            • Calculate an observance of an observation .
            • Factory function that returns a mixture of winners .
            • regress one helper function
            Get all kandi verified functions for this library.

            timemachines Key Features

            No Key Features are available at this moment for timemachines.

            timemachines Examples and Code Snippets

            No Code Snippets are available at this moment for timemachines.

            Community Discussions

            QUESTION

            How do I instrument region and environment information correctly in Prometheus?
            Asked 2022-Mar-09 at 17:53

            I've an application, and I'm running one instance of this application per AWS region. I'm trying to instrument the application code with Prometheus metrics client, and will be exposing the collected metrics to the /metrics endpoint. There is a central server which will scrape the /metrics endpoints across all the regions and will store them in a central Time Series Database.

            Let's say I've defined a metric named: http_responses_total then I would like to know its value aggregated over all the regions along with individual regional values. How do I store this region information which could be any one of the 13 regions and env information which could be dev or test or prod along with metrics so that I can slice and dice metrics based on region and env?

            I found a few ways to do it, but not sure how it's done in general, as it seems a pretty common scenario:

            I'm new to Prometheus. Could someone please suggest how I should store this region and env information? Are there any other better ways?

            ...

            ANSWER

            Answered 2022-Mar-09 at 17:53

            All the proposed options will work, and all of them have downsides.

            The first option (having env and region exposed by the application with every metric) is easy to implement but hard to maintain. Eventually somebody will forget to about these, opening a possibility for an unobserved failure to occur. Aside from that, you may not be able to add these labels to other exporters, written by someone else. Lastly, if you have to deal with millions of time series, more plain text data means more traffic.

            The third option (storing these labels in a separate metric) will make it quite difficult to write and understand queries. Take this one for example:

            Source https://stackoverflow.com/questions/71408188

            QUESTION

            Amazon EKS (NFS) to Kubernetes pod. Can't mount volume
            Asked 2021-Nov-10 at 02:26

            I'm working on attaching Amazon EKS (NFS) to Kubernetes pod using terraform.

            Everything runs without an error and is created:

            • Pod victoriametrics
            • Storage Classes
            • Persistent Volumes
            • Persistent Volume Claims

            However, the volume victoriametrics-data doesn't attach to the pod. Anyway, I can't see one in the pod's shell. Could someone be so kind to help me understand where I'm wrong, please?

            I have cut some unimportant code for the question to get code shorted.

            ...

            ANSWER

            Answered 2021-Nov-10 at 02:26

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

            Source https://stackoverflow.com/questions/69902046

            QUESTION

            InfluxDB not starting: 8086 bind address already in use
            Asked 2021-Oct-07 at 15:50

            I have an InfluxDB Version 1.8.9, but I can't start it. In this example I'm logged in as a root.

            ...

            ANSWER

            Answered 2021-Sep-21 at 17:57

            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:

            Source https://stackoverflow.com/questions/69272620

            QUESTION

            Writing the data to the timeseries database over unstable network
            Asked 2021-Sep-14 at 22:08

            I'm trying to find a time series database for the following scenario:

            1. Some sensor on raspberry pi provides the realtime data.
            2. Some application takes the data and pushes to the time series database.
            3. If network is off (GSM modem ran out of money or rain or something else), store data locally.
            4. Once network is available the data should be synchronised to the time series database in the cloud. So no missing data and no duplicates.
            5. (Optionally) query database from Grafana

            I'm looking for time series database that can handle 3. and 4. for me. Is there any?

            I can start Prometheus in federated mode (Can I?) and keep one node on raspberry pi for initial ingestion and another node in the cloud for collecting the data. But that setup would instantly consume 64mb+ of memory for Prometheus node.

            ...

            ANSWER

            Answered 2021-Sep-14 at 22:08

            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.

            Source https://stackoverflow.com/questions/69180563

            QUESTION

            Recommended approach to store multi-dimensional data (e.g. spectra) in InfluxDB
            Asked 2021-Sep-05 at 11:04

            I am trying to incorporate the time series database with the laboratory real time monitoring equipment. For scalar data such as temperature the line protocol works well:

            ...

            ANSWER

            Answered 2021-Sep-05 at 11:04

            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.

            Source https://stackoverflow.com/questions/69008057

            QUESTION

            What to report in a time serie database when the measure failed?
            Asked 2021-Jun-08 at 13:53

            I use a time series database to report some network metrics, such as the download time or DNS lookup time for some endpoints. However, sometimes the measure fails like if the endpoint is down, or if there is a network issue. In theses cases, what should be done according to the best practices? Should I report an impossible value, like -1, or just not write anything at all in the database?

            The problem I see when not writing anything, is that I cannot know if my test is not running anymore, or if it is a problem with the endpoint/network.

            ...

            ANSWER

            Answered 2021-Jun-08 at 13:53

            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.

            Source https://stackoverflow.com/questions/67701340

            QUESTION

            R ggplot customize month labels in time series
            Asked 2021-May-18 at 21:58

            I have a database that is being used to create a time series. The date column in the time series database is formatted as a POSIXct date format.

            Database ...

            ANSWER

            Answered 2021-May-18 at 21:58

            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

            Source https://stackoverflow.com/questions/67592610

            QUESTION

            How Can I Generate A Visualisation with Multiple Data Series In Splunk
            Asked 2021-Apr-29 at 13:11

            I have been experimenting with Splunk, trying to emulate some basic functionality from the OSISoft PI Time Series database.

            I have two data points that I wish to display trends for over time in order to compare fluctuations between them, specifically power network MW analogue tags.

            In PI this is very easy to do, however I am having difficulty figuring out how to do it in Splunk.

            How do I achieve this given the field values "SubstationA_T1_MW", & "SubstationA_T2_MW" in the field Tag?

            The fields involved are TimeStamp, Tag, Value, and Status

            Edit:

            Sample Input and Output listed below:

            ...

            ANSWER

            Answered 2021-Apr-29 at 12:41

            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:

            Source https://stackoverflow.com/questions/67304621

            QUESTION

            How can I deploy QuestDB on GCP?
            Asked 2021-Apr-08 at 09:38

            I would like to deploy the time series database QuestDB on GCP, but I do not see any instructions on the documentation. Could I get some steps?

            ...

            ANSWER

            Answered 2021-Apr-08 at 09:38

            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.

            Source https://stackoverflow.com/questions/66805126

            QUESTION

            Group By day for custom time interval
            Asked 2021-Mar-23 at 09:47

            I'm very new to SQL and time series database. I'm using crate database. I want to aggregate the data by day. But the I want to start each day start time is 9 AM not 12AM..

            Time interval is 9 AM to 11.59 PM.

            Unix time stamp is used to store the data. following is my sample database.

            ...

            ANSWER

            Answered 2021-Mar-23 at 09:47

            You want to add nine hours to midnight:

            Source https://stackoverflow.com/questions/66759638

            Community Discussions, Code Snippets contain sources that include Stack Exchange Network

            Vulnerabilities

            No vulnerabilities reported

            Install timemachines

            You can install using 'pip install timemachines' or download it from GitHub, PyPI.
            You can use timemachines like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

            Support

            See CONTRIBUTE.md. Also FAQ.
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            Install
          • PyPI

            pip install timemachines

          • CLONE
          • HTTPS

            https://github.com/microprediction/timemachines.git

          • CLI

            gh repo clone microprediction/timemachines

          • sshUrl

            git@github.com:microprediction/timemachines.git

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