Rethink Time and Data in Your Organization
Unlike the wishes Cher sings in her famous song, you cannot turn back time. But with the tools that have become available, you have a better chance of predicting time or, more accurately, predicting if occurrences in a time series sample will continue a decision-influencing trend.
Facebook Prophet and TensorFlow, issued by Google, are two machine learning protocols aimed at enticing developers to create exciting data science applications. Technology and analytics managers should view these tools as ways to expand their DataOps capabilities and expand their initial steps into machine learning.
Created by the Facebook core data science team, Facebook Prophet provides a reliable time series forecast where processing capacity is an issue. Prophet is based on an additive model to address how non-linear trends fit with yearly, weekly, and daily seasonality. The framework aids businesses when data contains periodic trends, such as retail holidays or the discovery that a sudden event impacted a trend. R programming and Python versions were launched a year ago, so businesses can leverage open source resources to create models. The source code and examples are available on GitHub.
I have previously reported on TensorFlow — you can read about it here. The neural network framework also offers an additional suite of probability models; in R the models are called as a separate library. This allows for more advanced statistical models to be built into the model easily. In the case of time series, users can apply a Bayesian structural time series. A Bayesian structural time series is a set of probability models that includes and generalizes many standard time-series modeling concepts. Its purpose is to highlight statistical details for more accurate comparisons between time series data of current and previous periods. The TensorFlow probability library allows a model to incorporate the Bayesian Structural Time Series.