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.

***

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**.

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

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