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Investigate IoT Time Series Applications #3

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antoinecarme opened this issue Oct 31, 2016 · 8 comments
Open

Investigate IoT Time Series Applications #3

antoinecarme opened this issue Oct 31, 2016 · 8 comments

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@antoinecarme
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antoinecarme commented Oct 31, 2016

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.

@antoinecarme antoinecarme changed the title Investiuagte IoT Time Series Applications Investiagte IoT Time Series Applications Oct 31, 2016
@antoinecarme antoinecarme changed the title Investiagte IoT Time Series Applications Investigate IoT Time Series Applications Oct 31, 2016
@antoinecarme
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@antoinecarme
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antoinecarme commented Oct 31, 2016

@antoinecarme
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antoinecarme commented Dec 20, 2016

(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

@antoinecarme
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Gorilla: A Fast, Scalable, In-Memory Time Series Database

http://www.vldb.org/pvldb/vol8/p1816-teller.pdf

@antoinecarme
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antoinecarme commented Jan 29, 2017

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https://code.facebook.com/posts/952820474848503/beringei-a-high-performance-time-series-storage-engine/

Beringei: A high-performance time series storage engine

@antoinecarme
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antoinecarme commented Feb 27, 2017

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:

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