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Investigate IoT Time Series Applications #3
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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 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. |
Gorilla: A Fast, Scalable, In-Memory Time Series Database |
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: |
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.
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