12 min
Is Facebook s Prophet the Time-Series Messiah, or Just a Very Naughty Boy? Published on February 3, 2021
Facebook s Prophet package aims to provide a simple, automated approach to prediction of a large number of different time series. The package employs an easily interpreted, three component additive model whose Bayesian posterior is sampled using STAN. In contrast to some other approaches, the user of Prophet might hope for good performance without tweaking a lot of parameters. Instead, hyper-parameters control how likely those parameters are a priori, and the Bayesian sampling tries to sort things out when data arrives.
Judged by popularity, this is surely a good idea. Facebook s prophet package has been downloaded 13,698,928 times according to pepy. It tops the charts, or at least the one I compiled here where hundreds of Python time series packages were ranked by monthly downloads. Download numbers are easily gamed and deceptive but no
In the previous article in the series, we introduced the Tangle as a data structure. We also learned about tips, and the importance of choosing a good tip selection algorithm. Today we will learn about transaction rates and network latency, and the role they play in determining the shape of
Baidu releases patents for epidemic prediction methods Baidu Inc. recently released an invention patent for "prediction methods, devices, equipment, . Artificial Intelligence, Baidu, Beijing, Chongqing, Guangzhou, Hangzhou, Sequoia, Sequoia Capital China, Shanghai, shenzhen, Shenzhen capital group
The optimal intelligent model could be disaggregation
Joseph Martins Thu 13 May 2021 // 07:30 UTC Share
Copy
Sponsored Over the last two decades, enterprises have gotten datacenter management down to a fine art. Standardization and automation means improved efficiency, both in terms of raw compute and of power consumption. Technologies such as virtualization and containerization mean users and developers can make more efficient use of resources, to the point of enabling self-service deployment.
However, the general purpose x86 architectures that fuel modern datacenters are simply not appropriate for running AI workloads. AI researchers got round this by repurposing GPU technology to accelerate AI operations, and this is what has fuelled the breakneck innovation in machine learning over the last decade or so.