TL;DR: Moirai is a cutting-edge time series foundation model, offering universal forecasting capabilities. It stands out as a versatile time series forecasting model capable of addressing diverse forecasting tasks across multiple domains, frequencies, and variables in a zero-shot manner.  To achieve this, Moirai tackles four major challenges: (i) construction

TL;DR LogAI is an open-source library designed for log analytics and intelligence. It can process raw logs generated by computer systems and support log analytics tasks such as log clustering and summarization, as well as log intelligence tasks such as log anomaly detection and root-cause analysis. LogAI is compatible

TL;DR: The performance of existing time-series forecasting methods can degrade due to non-stationarity, where the statistical distribution of time-series data changes over time. Our new DeepTime method overcomes non-stationarity issues by leveraging a “forecasting as meta-learning” framework on deep time-index models. DeepTime achieves competitive accuracy on the long-sequence time-series

TL;DR: We developed a new time-series forecasting model called ETSformer that leverages the power of two frameworks. By combining the classical intuition of seasonal-trend decomposition and exponential smoothing with modern transformers – as well as introducing novel exponential smoothing and frequency attention mechanisms – ETSformer achieves state-of-the-art performance. Background Before diving