0 bookmark(s) - Sort by: Date ↓ / Title /
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.
This article provides a roundup of notable time-series forecasting papers published between 2023 and 2024. It highlights five influential papers, including a case study from the online fashion industry, a review on forecasting reconciliation, and new deep learning models like TSMixer and CARD. The article emphasizes advancements in forecasting models, handling challenges in retail forecasting, and improvements in hierarchical forecasting methods.
The article discusses methods for data scientists to answer 'what if' questions regarding the impact of actions or events without having conducted prior experiments. It focuses on creating counterfactual predictions using machine learning techniques and compares a proposed method with Google's Causal Impact. The approach involves using historical data and control groups to estimate the effect of modifications, addressing challenges such as seasonality, confounders, and temporal drift.
TimesFM is a pretrained time-series foundation model developed by Google Research for time-series forecasting, focusing on point forecasts for univariate time series up to 512 time points with any horizon length and an optional frequency indicator.
This article discusses Time-MOE, an open-source time-series foundation model using Mixture-of-Experts (MOE) to improve forecasting accuracy while reducing computational costs. Key contributions include the Time-300B dataset, scaling laws for time series, and the Time-MOE architecture.
A deep dive into time series analysis and forecasting methods, providing foundational knowledge and exploring various techniques used for understanding past data and predicting future outcomes.
Article discusses a study at MIT Data to AI Lab comparing large language models (LLMs) with other methods for detecting anomalies in time series data. Despite losing to other methods, LLMs show potential for zero-shot learning and direct integration in deployment, offering efficiency gains.
The article discusses an interactive machine learning tool that enables analysts to interrogate modern forecasting models for time series data, promoting human-machine teaming to improve model management in telecoms maintenance.
ASCVIT V1 aims to make data analysis easier by automating statistical calculations, visualizations, and interpretations.
Includes descriptive statistics, hypothesis tests, regression, time series analysis, clustering, and LLM-powered data interpretation.
Integrates with an LLM (large language model) via Ollama for automated interpretation of statistical results.
Alibaba Cloud has developed a new tool called TAAT that analyzes log file timestamps to improve server fault prediction and detection. The tool, which combines machine learning with timestamp analysis, saw a 10% improvement in fault prediction accuracy.
First / Previous / Next / Last / Page 1 of 0