https://github.com/frutik/awesome-timeseries
Forecasting, anomaly detection, predictive analytics, econometrics and much more
https://github.com/frutik/awesome-timeseries
List: awesome-timeseries
anomaly-detection econometrics forecasting predictive-analytics timeseries
Last synced: 4 months ago
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Forecasting, anomaly detection, predictive analytics, econometrics and much more
- Host: GitHub
- URL: https://github.com/frutik/awesome-timeseries
- Owner: frutik
- Created: 2020-05-09T07:01:38.000Z (about 6 years ago)
- Default Branch: master
- Last Pushed: 2020-06-29T08:01:34.000Z (almost 6 years ago)
- Last Synced: 2025-11-10T19:01:48.691Z (8 months ago)
- Topics: anomaly-detection, econometrics, forecasting, predictive-analytics, timeseries
- Homepage:
- Size: 7.81 KB
- Stars: 16
- Watchers: 2
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
- awesome-search - Awesome time series
README
# awesome-timeseries
Forecasting, anomaly detection, predictive analytics, econometrics and much more
# General
* [Forecasting: Principles and Practice](https://otexts.com/fpp2/)
* [Time series of counts](https://otexts.com/fpp2/counts.html)
* [Time series data mining techniques and applications](https://towardsdatascience.com/time-series-data-mining-techniques-and-applications-5159b8ad26ec)
* [3 facts about time series forecasting that surprise experienced machine learning practitioners.](https://towardsdatascience.com/3-facts-about-time-series-forecasting-that-surprise-experienced-machine-learning-practitioners-69c18ee89387)
* [Seasonality in Python: additive or multiplicative model?](https://medium.com/@sigmundojr/seasonality-in-python-additive-or-multiplicative-model-d4b9cf1f48a7)
* [How to forecast sales with Python using SARIMA model](https://towardsdatascience.com/how-to-forecast-sales-with-python-using-sarima-model-ba600992fa7d)
* [Time Series: why do we need Stationarity and Ergodicity](https://medium.com/datadriveninvestor/time-series-why-do-we-need-stationarity-and-ergodicity-f34d2a344458)
* [Machine Learning for Time Series Data](https://medium.com/@ODSC/machine-learning-for-time-series-data-e3971d38005b)
* How (not) to use Machine Learning for time series forecasting: [Avoiding the pitfalls](https://towardsdatascience.com/how-not-to-use-machine-learning-for-time-series-forecasting-avoiding-the-pitfalls-19f9d7adf424), [The sequel](https://towardsdatascience.com/how-not-to-use-machine-learning-for-time-series-forecasting-the-sequel-e117e6ff55f1)
* [Building a real-time anomaly detection system for time series at Pinterest](https://medium.com/pinterest-engineering/building-a-real-time-anomaly-detection-system-for-time-series-at-pinterest-a833e6856ddd)
# Tools
* [4 Best Time Series Databases To Watch in 2019](https://medium.com/schkn/4-best-time-series-databases-to-watch-in-2019-ef1e89a72377)
## Timescaledb
* [Tutorial: Time-Series Forecasting](https://docs.timescale.com/latest/tutorials/tutorial-forecasting)
## Influxdb
* [Forecasting with FB Prophet and InfluxDB](https://www.influxdata.com/blog/forecasting-with-fb-prophet-and-influxdb/)
* [How to use InfluxDB’s built-in multiplicative Holt-Winters function to generate predictions on time series data](https://www.influxdata.com/blog/how-to-use-influxdbs-holt-winters-function-for-predictions/)
## Prophet
* [Quick Start](https://facebook.github.io/prophet/docs/quick_start.html#python-api)
* [Predicting the ‘Future’ with Facebook’s Prophet](https://towardsdatascience.com/predicting-the-future-with-facebook-s-prophet-bdfe11af10ff)
Forecasting Medium’s statistics using Facebook’s Prophet Library
* [Forecasting in Python with Facebook Prophet](https://towardsdatascience.com/forecasting-in-python-with-facebook-prophet-29810eb57e66)
How to tune and optimize Prophet using domain knowledge to add greater control to your forecasts.
* [Forecasting with Prophet](https://towardsdatascience.com/forecasting-with-prophet-d50bbfe95f91) 🔭
How to make high quality forecasts
* [The Math of Prophet](https://medium.com/future-vision/the-math-of-prophet-46864fa9c55a)
Breaking down the Equation behind Facebook’s open-source Time Series Forecasting procedure
* [Time Series Forecasting with Prophet](https://towardsdatascience.com/time-series-forecasting-with-prophet-54f2ac5e722e)
Learn how to use Facebook’s Prophet to predict air quality
#### Issues
* [Prophet for Intermittent demand forecasting](https://github.com/facebook/prophet/issues/1442)
* [Working with data of many 0s and 1s and very few 2,3 and 4s](https://github.com/facebook/prophet/issues/1153)
## ELK
* [Analyzing the past and present](https://www.elastic.co/guide/en/machine-learning/7.6/ml-overview.html)
# Misc
* [Champange sales forecasting](https://github.com/palashmoon/champange-sales-forecasting)
* https://github.com/MaxBenChrist/awesome_time_series_in_python