Competitive Advantages
- Making it easier to train subsequent machine learning/AI algorithms in tasks involving prediction or classification
- Providing a reliable and robust representation for the time-series data as noise and other outliers are removed during the weighted averaging operation.
- Providing a scalable solution where one can analyze time-series data with any length in duration including any number of variables.
- Improving memory allocation and computational speeds to due to compressing of potentially very high-volume data into a rich yet compact representative version.
Summary
Whenever a sensor network is implemented, for instance in a transportation setting, especially for a multitude of different scenarios including shipment locations, durations and modes, the data being collected by these sensors invariably becomes heterogeneous with different profiles and durations. This creates multi-variate time-series datasets spanning time periods which can be different by orders of magnitude and subsequently a challenging scenario for anyone who wants to gain actionable insight from vast amounts of data. In this work we propose a brand-new approach to how we can better summarize and visualize multi-variate time series data coming from numerous sources in a sensor network distributed across a wide range of application scenarios to gather actionable and robust insight while ensuring accurate analytics down the information chain including prediction and classification of data. This representative signal can be either for a particular location in the sensor network distributed across different applications or, vice versa, it could be for the application itself distributed across different locations in the sensor network which provides greater flexibility when different points of view are needed to analyze a situation. This method has ability to reduce operational costs by unlocking the true advantage of using sensor networks in the production and distribution of goods
Cold-chain time-series Based Machine Learning Pipeline. The images show the real environment for data collection in this work.
Desired Partnerships
- License
- Sponsored Research
- Co-Development