Looking for Guidance on Applying Hierarchical Temporal Memory (HTM) to IoT Data Anomaly Detection

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Hello Everyone :hugs:,

At present, I am engaged in a project that utilises Hierarchical Temporal Memory (HTM) that to detect anomalies in Internet of Things (IoT) data. As you may already be aware, conventional anomaly detection techniques have particular difficulties when dealing with the continuous, real-time, and frequently multi-dimensional nature of IoT data.

After doing a lot of study, I think that HTM could be an intriguing approach because it can model time sequences and discover patterns in an uncontrolled way.

This is a quick synopsis of the assignment and the difficulties I’m having:

Project Synopsis:

  • Data Source: Real-time data streams (such as temperature, humidity, and motion) are generated by a variety of IoT sensors.
  • Goal: Seek out anomalies that might point to problems like environmental shifts, equipment failures, or security breaches.
  • Current Progress: analysed the information that comes in streams, started basic training, and implemented a simple HTM model using NuPIC.

Problems:

  • Finding the ideal time and space pooler parameters to best capture the trends in the multifaceted IoT data is known as parameter tuning.
  • Scalability: ensuring that as data volume and sensor count increase, the HTM model can keep up.
  • Interpreting anomalies: Differentiating between true anomalies and false positives by interpreting the anomalies that the HTM model has identified.

Inquiries for the Community:

  1. Parameter Optimisation: How should the HTM parameters be adjusted for IoT data? What are some recommended tactics or best practices? :thinking: Exist any particular NuPIC settings or tools that could help with this? :thinking:

  2. Managing High Dimensionality: In HTM, what are some effective ways to manage and process high-dimensional data? :thinking: Which methods or stages of preprocessing have you found to be effective? :thinking:

  3. Practical Uses: Have either of you used HTM for comparable practical uses? If yes, could you tell us about your experiences? :thinking: In particular, any difficulties you had and how you resolved them? :thinking:

I also followed this :point_right: https://www.researchgate.net/publication/319486374_Hierarchical_Temporal_Memory_Method_for_Time-series-based_Anomaly_Detection_Mlops but didn’t get any clarification on that.

I would be interested in hearing your opinions, recommendations, and any sources or reference you may have. Your knowledge and experience will be very helpful to my undertaking, and I’m looking forward to having a fruitful conversation with you.

Thank you :pray: in advance.

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