Time-Series Anomaly Detection ML
Budget: $30 – $250 USD
I'm looking for an experienced machine learning specialist to develop an algorithm for time-series anomaly detection using sensor data. The sensor is a GNSS sensor. The data are navigation satellite parameters that have developed a daily pattern. The goal is for the model to learn the pattern of these parameters so that when disturbances occur, the model can classify them as anomalies.The anomalies are spikes, drops, and outliers.
Key Requirements:
- Analyze sensor data
- Detect anomalies: spikes, drops, outliers
- Focus on improving prediction accuracy
Ideal Skills and Experience:
- Expertise in machine learning and time-series analysis
- Proficiency in Python and relevant libraries (e.g., TensorFlow, Keras, Scikit-learn)
- Experience with anomaly detection algorithms
- Strong background in data preprocessing and feature engineering
- Ability to validate and test models effectively
Key Requirements:
- Analyze sensor data
- Detect anomalies: spikes, drops, outliers
- Focus on improving prediction accuracy
Ideal Skills and Experience:
- Expertise in machine learning and time-series analysis
- Proficiency in Python and relevant libraries (e.g., TensorFlow, Keras, Scikit-learn)
- Experience with anomaly detection algorithms
- Strong background in data preprocessing and feature engineering
- Ability to validate and test models effectively