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General\n\n* [Forecasting: Principles and Practice](https://otexts.com/fpp2/)\n* [Time series of counts](https://otexts.com/fpp2/counts.html)\n* [Time series data mining techniques and applications](https://towardsdatascience.com/time-series-data-mining-techniques-and-applications-5159b8ad26ec)\n* [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)\n* [Seasonality in Python: additive or multiplicative model?](https://medium.com/@sigmundojr/seasonality-in-python-additive-or-multiplicative-model-d4b9cf1f48a7)\n* [How to forecast sales with Python using SARIMA model](https://towardsdatascience.com/how-to-forecast-sales-with-python-using-sarima-model-ba600992fa7d)\n* [Time Series: why do we need Stationarity and Ergodicity](https://medium.com/datadriveninvestor/time-series-why-do-we-need-stationarity-and-ergodicity-f34d2a344458)  \n* [Machine Learning for Time Series Data](https://medium.com/@ODSC/machine-learning-for-time-series-data-e3971d38005b)\n* 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)  \n* [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)\n\n\n# Tools\n\n* [4 Best Time Series Databases To Watch in 2019](https://medium.com/schkn/4-best-time-series-databases-to-watch-in-2019-ef1e89a72377)\n\n## Timescaledb\n\n* [Tutorial: Time-Series Forecasting](https://docs.timescale.com/latest/tutorials/tutorial-forecasting)\n\n## Influxdb\n\n* [Forecasting with FB Prophet and InfluxDB](https://www.influxdata.com/blog/forecasting-with-fb-prophet-and-influxdb/)\n* [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/)\n\n## Prophet\n\n* [Quick Start](https://facebook.github.io/prophet/docs/quick_start.html#python-api)\n* [Predicting the ‘Future’ with Facebook’s Prophet](https://towardsdatascience.com/predicting-the-future-with-facebook-s-prophet-bdfe11af10ff)\n  \n  Forecasting Medium’s statistics using Facebook’s Prophet Library\n  \n* [Forecasting in Python with Facebook Prophet](https://towardsdatascience.com/forecasting-in-python-with-facebook-prophet-29810eb57e66)\n\n   How to tune and optimize Prophet using domain knowledge to add greater control to your forecasts.\n   \n* [Forecasting with Prophet](https://towardsdatascience.com/forecasting-with-prophet-d50bbfe95f91) 🔭\n  \n  How to make high quality forecasts\n\n* [The Math of Prophet](https://medium.com/future-vision/the-math-of-prophet-46864fa9c55a)\n  \n  Breaking down the Equation behind Facebook’s open-source Time Series Forecasting procedure\n  \n* [Time Series Forecasting with Prophet](https://towardsdatascience.com/time-series-forecasting-with-prophet-54f2ac5e722e)\n  \n  Learn how to use Facebook’s Prophet to predict air quality\n\n#### Issues\n\n* [Prophet for Intermittent demand forecasting](https://github.com/facebook/prophet/issues/1442)\n* [Working with data of many 0s and 1s and very few 2,3 and 4s](https://github.com/facebook/prophet/issues/1153)\n\n\n## ELK\n\n* [Analyzing the past and present](https://www.elastic.co/guide/en/machine-learning/7.6/ml-overview.html)\n\n# Misc\n\n* [Champange sales forecasting](https://github.com/palashmoon/champange-sales-forecasting)\n* https://github.com/MaxBenChrist/awesome_time_series_in_python\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ffrutik%2Fawesome-timeseries","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ffrutik%2Fawesome-timeseries","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ffrutik%2Fawesome-timeseries/lists"}