Rare event prediction on time series that change structure mid-stream? [D]

“`html

A recent post on r/MLQuestions discusses a challenging scenario where a machine learning engineer is working on predicting failures for approximately 33,000 chargers. These devices operate at two distinct rates depending on their current state: high-frequency (1 observation every 20 seconds) when active and low-frequency (1 observation per hour) otherwise.

The primary challenge in this setup is the frequent mode shifts between idle and active states, which can complicate the model’s ability to capture long-term patterns. The positive failure rate for these chargers is relatively low at around 2% over a period of 90 days, with significant per-device variance in usage patterns.

The engineer is considering two strategies: using one or two separate recurrent neural network (RNN) encoders for each operational state and then merging their outputs into a single decoder. This approach aims to handle the mode shift problem at the architecture level.
Another option being explored involves windowing and sampling techniques, which could help manage the varying data rates and ensure that all observations are included in the model’s training process.
The engineer is also looking for advice on how to effectively reweight or skew losses when dealing with such low failure rates—currently around 2%—in time series data. This situation requires careful handling to avoid overfitting, especially since the positive rate is well below what many typical machine learning models are designed to handle.

“`

– Takeaways:
– The engineer faces a unique challenge due to frequent mode shifts between different operational states.
– Two architectural approaches—using separate encoders and windowing/sampling—are being considered.
– Effective handling of low positive rates is crucial, as this impacts the model’s performance significantly.

Originally published at reddit.com. Curated by AI Maestro.

Stay ahead of AI. Get the most important stories delivered to your inbox — no spam, no noise.