pyaf
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antoinecarme
Investigate IoT Time Series Applications
At least check the possibility of using pyaf in this context. pyaf is not aware of the data source type (time series database or web service, etc) as long as the dataset is stored in a pandas dataframe. Is there a link with hierarchical models ? A jupyter notebook is welcome with a real example.
Similar work https://developer.ibm.com/recipes/tutorials/timeseries-data-analysis-of-iot-events-by-using-jupyter-notebook/
And a Watson sample notebook : https://github.com/ibm-watson-iot/predictive-analytics-samples/blob/master/Notebook/TimeseriesDataAnalysis.ipynb
coming from
https://github.com/ibm-watson-iot/predictive-analytics-samples/
thanks to @ibm-watson-iot
(by @cityzendata)
Warp 10 Platform Introduction
Warp 10 is an Open Source Geo Time Series® Platform designed to handle data coming from sensors, monitoring systems and the Internet of Things.
Geo Time Series® extend the notion of Time Series by merging the sequence of sensor readings with the sequence of sensor locations. If your data have no location information, Warp 10 will handle them as regular Time Series.
https://github.com/cityzendata/warp10-platform
Gorilla: A Fast, Scalable, In-Memory Time Series Database
http://www.vldb.org/pvldb/vol8/p1816-teller.pdf
Time Series DBMS
http://db-engines.com/en/ranking http://db-engines.com/en/article/Time+Series+DBMS
http://renesd.blogspot.fr/2017/02/is-postgresql-good-enough.html https://github.com/tgres/tgres
https://code.facebook.com/posts/952820474848503/beringei-a-high-performance-time-series-storage-engine/
Beringei: A high-performance time series storage engine
Prophet: forecasting at scale
https://research.fb.com/prophet-forecasting-at-scale/?
Today Facebook is open sourcing Prophet, a forecasting tool available in Python and R. Forecasting is a data science task that is central to many activities within an organization. For instance, large organizations like Facebook must engage in capacity planning to efficiently allocate scarce resources and goal setting in order to measure performance relative to a baseline. Producing high quality forecasts is not an easy problem for either machines or for most analysts. We have observed two main themes in the practice of creating a variety of business forecasts:
