This article demonstrates how to use the attention mechanism in a time series classification framework, specifically for classifying normal sine waves versus 'modified' (flattened) sine waves. It details the data generation, model implementation (using a bidirectional LSTM with attention), and results, achieving high accuracy.
This paper introduces Toto, a time series forecasting foundation model with 151 million parameters, and BOOM, a large-scale benchmark for observability time series data. Toto uses a decoder-only architecture and is trained on a large corpus of observability, open, and synthetic data. Both Toto and BOOM are open-sourced under the Apache 2.0 License.
Datadog announces the release of Toto, a state-of-the-art open-weights time series foundation model, and BOOM, a new observability benchmark. Toto achieves SOTA performance on observability metrics, and BOOM provides a challenging dataset for evaluating time series models in the observability domain.
This article provides a hands-on guide to classifying human activity using sensor data and machine learning. It covers preparing data, creating a feature extraction pipeline using TSFresh, training a machine learning classifier with scikit-learn, and validating the model using the Data Studio.
SHREC is a physics-based unsupervised learning framework that reconstructs unobserved causal drivers from complex time series data. This new approach addresses the limitations of contemporary techniques, such as noise susceptibility and high computational cost, by using recurrence structures and topological embeddings. The successful application of SHREC on diverse datasets highlights its wide applicability and reliability in fields like biology, physics, and engineering, improving the accuracy of causal driver reconstruction.
Outlier treatment is a necessary step in data analysis. This article, part 3 of a four-part series, eases the process and provides insights on effective methods and tools for outlier detection.
A powerful library by Amazon — coding example included
