#javascript #state_management #time_travel #vue #vuex

https://github.com/vuejs/vuex

#javascript #2019_ncov #api #coronavirus #covid_19 #data #dataset #json #time_series

https://github.com/pomber/covid19

#html #artificial_intelligence #automl #azure_ml #best_practices #deep_learning #demand_forecasting #dilated_cnn #forecasting #hyperparameter_tuning #jupyter_notebook #lightgbm #machine_learning #model_deployment #prophet #python #r #retail #statistical_models #tidyverse #time_series

https://github.com/microsoft/forecasting

#jupyter_notebook #augmentation #data_augmentation #data_science #feature_engineering #feature_extraction #machine_learning #tabular_data #time_series

https://github.com/firmai/deltapy

#typescript #electron_app #jira #linux #macos #task_manager #time_tracker

https://github.com/johannesjo/super-productivity

#python #forecasting #forecasting_models #machine_learning #time_series

https://github.com/unit8co/darts

#cplusplus #chart #csv #dataviz #plot #px4 #qt5 #ros #rosbag #time_series #ulog

https://github.com/facontidavide/PlotJuggler

#python #deep_learning #forecasting #pytorch #self_attention #time_series

https://github.com/zhouhaoyi/Informer2020

#python #deep_learning #forecasting #machine_learning #mxnet #neural_networks #pytorch #time_series #time_series_forecasting #time_series_prediction

https://github.com/awslabs/gluon-ts

#java #database #grafana #hacktoberfest #influxdb #iot #low_latency #metrics #monitoring #postgres #postgresql #questdb #simd #sql #time_series #timeseries_database #tsdb

https://github.com/questdb/questdb

#jupyter_notebook #arima #econometrics #forecasting #machine_learning #python #statistics #time_series #timeseries

https://github.com/Nixtla/statsforecast

#jupyter_notebook #articles #artificial_intelligence #data_analysis #data_science #data_visualization #machine_learning #natural_language_processing #python #scraping #time_series

https://github.com/khuyentran1401/Data-science

#python #data_mining #data_science #forecasting #machine_learning #scikit_learn #time_series #time_series_analysis #time_series_classification #time_series_regression

https://github.com/sktime/sktime

#c_lang #bigdata #cloud_native #cluster #connected_vehicles #database #distributed #financial_analysis #industrial_iot #iot #metrics #monitoring #scalability #sql #tdengine #time_series #time_series_database #tsdb

TDengine is a powerful, open-source time-series database designed for handling large amounts of data from IoT devices, connected cars, and industrial IoT. Here are the key benefits It can handle billions of data collection points efficiently, outperforming other time-series databases in data ingestion, querying, and compression.
- **Simplified Solution** Designed for cloud environments, it supports distributed design, sharding, partitioning, and Kubernetes deployment.
- **Ease of Use** Makes data exploration and access efficient through features like super tables and pre-computation.
- **Open Source**: Available under open source licenses with an active developer community.

Using TDengine helps you manage and analyze large-scale time-series data efficiently, making it ideal for various IoT and industrial applications.

https://github.com/taosdata/TDengine

#go #alerting #ccf #metrics #monitoring #nightingale #observability #open_falcon #time_series #tsdb

Nightingale is a free, open-source monitoring tool designed for cloud environments. It combines data collection, visualization, monitoring alerts, and data analysis into one platform. This tool supports multiple time-series databases like Prometheus and VictoriaMetrics, and it has advanced alert management features, including alert rules and notifications. It also offers high-performance visualization with various dashboard templates and supports different data collectors. Using Nightingale, you can manage alerts efficiently, reduce disturbances, and ensure seamless collaboration. This makes it a powerful tool for enterprises to monitor and analyze their systems effectively.

https://github.com/ccfos/nightingale

#python #autogluon #automated_machine_learning #automl #computer_vision #data_science #deep_learning #ensemble_learning #forecasting #gluon #hyperparameter_optimization #machine_learning #natural_language_processing #object_detection #python #pytorch #scikit_learn #structured_data #tabular_data #time_series #transfer_learning

AutoGluon makes machine learning easy and fast. With just a few lines of code, you can train and use high-accuracy models for images, text, time series, and tabular data. This means you can quickly build and deploy powerful machine learning models without needing to write a lot of code. It supports Python 3.8 to 3.11 and works on Linux, MacOS, and Windows, making it convenient for various users. This saves time and effort, allowing you to focus on other parts of your project.

https://github.com/autogluon/autogluon

#go #gnmi #golang #influxdb #json #kafka #logs #metrics #modbus #monitoring #mqtt #opcua #telegraf #time_series #windows_eventlog #windows_management_instrumentation #xpath

Telegraf is a tool that helps collect, process, and send various types of data like metrics, logs, and more. It has over 300 plugins for different tasks such as system monitoring, cloud services, and messaging. You can easily configure it using TOML, and it runs as a standalone binary without extra dependencies. This makes it easy to set up and use. With Telegraf, you can choose plugins to monitor your devices, logs, networks, and more, making it very flexible and powerful for managing your data efficiently.

https://github.com/influxdata/telegraf

#other #awesome #awesome_list #date #edge_cases #email #engineering #falsehoods #geography #identity #internationalization #myths #network #phone_number #phone_numbers #postal_addresses #programming #resources #time #typography

This resource is a curated list of common misconceptions, or "falsehoods," that programmers often believe about various topics such as time, email addresses, names, geography, and more. It helps programmers by highlighting and explaining these false beliefs, which can lead to errors and inefficiencies in coding. By understanding these falsehoods, developers can avoid common pitfalls and create more accurate and robust software. For example, knowing that email addresses can have multiple `@` characters or that time zones are more complex than they seem can prevent bugs and improve the overall quality of the code. This knowledge benefits users by ensuring the software they use is reliable and functions correctly.

https://github.com/kdeldycke/awesome-falsehood