We have a list of sensors, each of these is a chaotic time series that looks somehow like the attached picture. The different time series don't have the same length but contain are roughly 750 data points (some zero padding might be needed if you need to use all the time series at the same time)
While the time series corresponds to different values, the value 1 (the marked point in the graph) is an extremely crucial value for us.
We need to work with a person who is skilled in dealing with Chaotic Timeseries, that have used ESN / DeepESN before, and who is able to build a new architecture for a custom deep ESN in order to capture the prediction for the value 1.
The person would also need to work on hyperparamters optimization in order to find the best values for noise/radius
Dealing with ESN means good prediction for short forecast, and this goes perfectly with our need, as all we need is to predict the next point (forecast horizon = 1)
- A python Jupyter notebook containing the ESN hyperparameters and architecture and proof that it captures well the value 1.
While I think ESN should be a great fit here, i am not closed to other approaches and possible solutions, so please don't hesitate to share your point of view
it is strongly recommended that the interested candidates would present their approach and how confident they are in having strong predictors.
If this works successfully, we have other types of sensors that we would wish to explore as well, and we would be very happy to continue working with the selected talent.
PS: please don't apply if you have never worked on chaotic time series prediction, my time and yours are valuable; let's not waste it
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- Remote Job
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About the clientMember since Dec 13, 2020
- France Biot 11:07 am
8 jobs posted
50% hire rate, 3 open jobs
total spent 4 hires, 1 active
- Mid-sized company (10-99 people)
- Member since Dec 13, 2020
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