Machine learning for time series

Time series is sequentially revealed, time-stamped and time-critical data. People also call it "streaming data", "event streams" or "sequential data".

Just like "software is eating the world", time series are eating static data sets:

• The proliferation of time-stamped data follows naturally from the digitization of industry. The ongoing deployment of billions of connected sensors will only accelerate the trend.
• As a consequence, lots of decision-making processes that used to be fairly static (based on stable information) are becoming dynamic (based on streaming data).

The problem for operational managers is that standard machine learning solutions applied to time series underperform.

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This page provides short explanations and links to interesting resources about the three main aspects of machine learning for time series: preprocessing, 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 preprocessing steps required for efficient time series modeling.

• Recent trends in time series data storage and management
• Good overview of standard preprocessing techniques (in the context of Kaggle competitions)
• Great review of feature extraction techniques (in the context of stock price analysis)

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.

• Good explanation of some of the pitfalls of time series modeling
• Modeling options for streaming data
• A review of basic time series modeling concepts (with Python examples)
• Interesting thoughts on machine learning and demand forecasting
• Sharp comments on the inefficiency of neural networks
• High-level introduction to sequential aggregation

Congratulations, your machine learning prototype is completed. 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.

• Useful check-up list for machine learning prototypes about to go into production
• Lessons learned from building scalable machine learning pipelines
• A (technical) example of machine learning pipeline optimization
• A summary of Gartner's thoughts on operationalizing machine learning models