CarsonScott/CALM
Conditional Associative Logic Memory
This system helps professionals understand and forecast sequential events by recognizing recurring patterns in real-time data streams. It takes in a continuous flow of observations, learns the relationships between them, and outputs predictions about what is likely to happen next, along with the confidence of those predictions. Anyone working with time-series data or needing to anticipate future states based on past events, such as operations managers monitoring system logs or financial analysts tracking market movements, would find this useful.
No commits in the last 6 months.
Use this if you need to automatically identify sequential patterns in real-time data and predict future occurrences based on historical observations.
Not ideal if your data lacks sequential relationships or if you need to perform complex causal inference beyond conditional probabilities.
Stars
27
Forks
3
Language
Python
License
MIT
Category
Last pushed
Oct 30, 2017
Commits (30d)
0
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