AI News, Marrying Kalman Filtering Machine Learning

Marrying Kalman Filtering Machine Learning

Perhaps my search terms are not the best, perhaps Fintech guys keep such algorithms close to their vests, perhaps there is not much of work done in bringing these two incredibly powerful tools together...

am happy to report that pre-publication copy of my book (including MATLAB code) is available for download for FREE.

Condition monitoring = “How is my business doing today?” Preventive maintenance = “Predict issues and fix them before adverse business impact”.

In various sections of this book, I have repeated my observation from first-hand experience in Retail business that ML solutions in business are not “one and done” but “like flu shots” - adjust the mix and apply on a regular basis .

This is because I subscribe to a view of learning as “generalization from past experience AND results of new action” as the true definition of Learning!

In the applications listed above, “time constants” may be different – from milliseconds in Fintech to hours in Health and weeks and months in Retail.

This approach controls the “curse of dimensionality” since the number of Kalman States is not tied to the number of weights of the artificial neural network.

Also, much improvement and new developments are possible arising from my formulations here - hope you will create breakthrough algorithms!

How machine learning streamlines location data with the Kalman filter

Let’s quickly understand the constraints while capturing location and movement data: Tracking hardware Trackers send out location-based pings in the form of signals.

These tracking companies utilize the location data points (latitude-longitudes) of the resources movement to create a journey map for the trip.

While the tracker is sending out location pings, the tracker might send erroneous pings, which could offset the actual route of the resource.

Tracking software At the level of the software used to read and process the location information, there must be a built-in capability to clean and organize the incoming data.

Even at the planning stage, the bad data creates forecasting problems, which mean that companies won’t be able to accurately map their costs to their active resources, leading to over or under capacity.

Marrying Kalman Filtering Machine Learning

Perhaps my search terms are not the best, perhaps Fintech guys keep such algorithms close to their vests, perhaps there is not much of work done in bringing these two incredibly powerful tools together.

am happy to report that pre-publication copy of my book (including MATLAB code) is available for download for FREE.

In various sections of this book, I have repeated my observation from first-hand experience in Retail business that ML solutions in business are not “one and done” but “like flu shots” - adjust the mix and apply on a regular basis .

This is because I subscribe to a view of learning as “generalization from past experience AND results of new action” as the true definition of Learning!

In the applications listed above, “time constants” may be different – from milliseconds in Fintech to hours in Health and weeks and months in Retail.

This approach controls the “curse of dimensionality” since the number of Kalman States is not tied to the number of weights of the artificial neural network.

Also, much improvement and new developments are possiblearising frommy formulations here -hope you will create breakthrough algorithms!Andremember, ALL *business* machine learning problems are “in-stream” scenarios!

Time Series Forecasting with Splunk. Part I. Intro Kalman Filter.

After all, we need to construct a dashboard with such functionality: user selects a metric, specifies few tuning parameters, and gets an appropriate forecast of selected metric for a year.

For example, number of logins in night hours is constantly smaller than in day hours, as well as number of logins in weekdays is larger than on weekends.

Residual component is the difference between time series values and determinate components (trend, seasonality) and often stands for some kind of noise.

In the picture below you find some examples of stationary (blue) and non-stationary (yellow, red, violet) time series.

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