Research author
Ana Martins
2 AI research papers in the One9Founders library, with summaries and links to original sources.
Papers by Ana Martins
Fast and Interpretable Autoregressive Estimation with Neural Network Backpropagation
Anaísa Lucena, Ana Martins, Armando J. Pinho, et al.
This paper proposes a new method for estimating parameters in autoregressive (AR) time series models by using neural networks instead of traditional statistical methods. The key advantage is that the neural network approach is much faster (up to 34x speedup), more reliable (doesn't fail like conventional methods do 55% of the time), and still produces accurate, interpretable results comparable to traditional approaches.
Revisiting OmniAnomaly for Anomaly Detection: performance metrics and comparison with PCA-based models
Bruna Alves, Ana Martins, Armando J. Pinho, et al.
This paper compares OmniAnomaly, a popular deep learning model for detecting anomalies in time series data from multiple sources, against a simpler traditional statistical method called PCA. Using the same fair evaluation standards across 100 runs, the researchers found that the simpler PCA method performs just as well or even better than the complex deep learning model, suggesting that how we measure and compare these models matters more than we thought.