Time series forecasting is becoming increasingly important across various domains, thus having high-quality, diverse benchmarks are crucial for fair evaluation across model families.
We propose Moirai-MoE, the first mixture-of-experts time series foundation model, achieving token-level model specialization in a data-driven manner.
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,…
TL;DR: PyRCA is an open-source machine learning library specifically designed for conducting Root Cause Analysis (RCA) in IT operations. It offers a comprehensive framework that allows users to easily identify the complicated metric…
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…
AUTHORS: Chenghao Liu, Quang Pham, Doyen Sahoo, Donald Rose TL;DR: Nonstationary data, which changes its statistical properties over time, can make time series forecasting difficult. Despite the recent success of deep learning techniques…
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…
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…
