If you can measure it, consider it predicted
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Updated
May 9, 2024 - Jupyter Notebook
If you can measure it, consider it predicted
A Node metrics library for measuring and reporting application-level metrics, inspired by Coda Hale, Yammer Inc's Dropwizard Metrics Libraries
Predict time-series with one line of code.
API for manipulating time series on top of Apache Spark: lagged time values, rolling statistics (mean, avg, sum, count, etc), AS OF joins, downsampling, and interpolation
Time series analysis comprises methods for analyzing time series data in order to extract meaningful statistics and other characteristics of the data.
C++ library for Fearless Timeseries Logging
Simple app to generate hand-drawn time series
Time-Series Anomaly Detection Comprehensive Benchmark
Function to calculate consistency of phase at a given frequency across measurements
Collection of examples with LSTM recurrent neural networks
⏳ Time Series Tools R package provides a series of tools to simulate, plot, estimate, select and forecast different time series models.
NetCDF-CF Geometry and Timeseries Tools for R: https://code.usgs.gov/water/ncdfgeom
A package for accessing InfluxDB from Laravel 5.5+, based on configuration settings.
Ingest sample Market Orders Data feed from PubNub to Postgres with TimescaleDB extension installed and enabled for time series analysis.
A collection of data analysis tools for post-processing raw data and getting it into workable formats in python for further analysis
Performant, composable online learning
The repository provides a synthetic multivariate time series data generator. The implementation is an extention of the cylinder-bell-funnel time series data generator. The scipt enables synthetic data generation of different length, dimensions and samples.
openseries is a project with tools to analyze financial timeseries of a single asset or a group of assets. It is solely made for daily or less frequent data.
Basic time-series setup, using "Air Quality" dataset. The Data was recorded from March 2004 to February 2005 (one year) and will enable us to produce the following aggregations using only Redis to do the aggregations and operations on the data
Acquire and process live and historical air quality data without efforts. Filter by station-id, sensor-id and sensor-type, apply reverse geocoding, store into time-series and RDBMS databases, publish to MQTT, output as JSON, or visualize in Grafana. Data sources: Sensor.Community (luftdaten.info), IRCELINE, and OpenAQ.
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