Time series is sequentially revealed, time-stamped and time-critical data.
Just like "software is eating the world", time series are eating static data sets.
The problem for operational managers is that standard machine learning solutions applied to time series underperform.
This page provides short explanations and links to interesting resources about the three main aspects of machine learning for time series: pre-processing, modeling and post-processing.
You don't need perfect data streaming from a brand-new data lake to build valuable machine learning solution for time series. But you do need to handle your data carefully.
The resources below deal with the pre-processing steps required for efficient time series modeling.
Modeling is the core of the data scientist's jobs. The possibilities are endless, and the state of the art fast-moving.
Surprisingly given how pervasive time series are, the specificities of time series modeling are not well known. The resources below cover the main aspects of time series modeling.
Congratulations, your machine learning prototype is ready. Does it mean it is ready for production? Unfortunately not... Pre-processing and modeling are only half of the job. Software packaging (« post-processing ») is the other half - technical, fastidious, but absolutely necessary.
The resources below describe the main post-processing tasks required for machine learning and time series.
Your time series in the right order, whatever their frequency.
Extract meaningful features in your data, sequentially. Spot and handle outliers automatically.
Hit the ground running with our diverse portfolio of built-in predictive models.
For advanced custom modeling, Datapred is compatible with most open-source machine learning libraries.
Forget selecting the best model: boost performance and versatility by using multiple models at the same time.
Optimize for the right objective, under realistic constraints.
Parallel architecture that optimizes computations at every modeling step.
Pre-built connectors for standard databases, and templates for connecting to non-standard databases and proprietary management systems.
Leverage Datapred’s graph structure to build consistent, flexible and robust machine learning workflows.
Rigorous, online and customizable measurement of your solution’s performance.
Automatic backups at each time step, in your chosen format, for final or intermediate results.
Create a sandbox and continually test the contribution of new data sources and modeling options to your original solution.