F1FantasyData VS awesome-TS-anomaly-detection

Compare F1FantasyData vs awesome-TS-anomaly-detection and see what are their differences.

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F1FantasyData awesome-TS-anomaly-detection
1 72
9 2,822
- -
0.0 0.0
over 1 year ago 3 months ago
GNU General Public License v3.0 only -
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
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F1FantasyData

Posts with mentions or reviews of F1FantasyData. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2021-04-11.
  • Price Data And Sentiment Data
    2 projects | /r/fantasyF1 | 11 Apr 2021
    I just wanted to share something i have been working on during my free time in the last week. I wanted to see how sentiment and prices were evolving with the diferent drivers and teams but i found that there was no data on this matter. So i decided to track this data myself. I'm sure this will be of use to some people too, so here is the data. https://github.com/EduardoFAFernandes/F1FantasyData Unfortunatly there are gaps and the first data i recorded was a couple of days after the first race. I'll try to keep this updated on a daily basis but no promises.

awesome-TS-anomaly-detection

Posts with mentions or reviews of awesome-TS-anomaly-detection. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2020-12-31.

What are some alternatives?

When comparing F1FantasyData and awesome-TS-anomaly-detection you can also consider the following projects:

time-series-transformers-review - A professionally curated list of awesome resources (paper, code, data, etc.) on transformers in time series.

Awesome-Geospatial - Long list of geospatial tools and resources

openHistorian - The Open Source Time-Series Data Historian

awesome-metric-learning - 😎 A curated list of awesome practical Metric Learning and its applications

Netdata - The open-source observability platform everyone needs

NAB - The Numenta Anomaly Benchmark

A3 - Inspired by recent advances in coverage-guided analysis of neural networks, we propose a novel anomaly detection method. We show that the hidden activation values contain information useful to distinguish between normal and anomalous samples. Our approach combines three neural networks in a purely data-driven end-to-end model. Based on the activation values in the target network, the alarm network decides if the given sample is normal. Thanks to the anomaly network, our method even works in strict semi-supervised settings. Strong anomaly detection results are achieved on common data sets surpassing current baseline methods. Our semi-supervised anomaly detection method allows to inspect large amounts of data for anomalies across various applications.

pyod - A Comprehensive and Scalable Python Library for Outlier Detection (Anomaly Detection)

MLflow - Open source platform for the machine learning lifecycle