Blog

TLDR; We introduce the Salesforce CausalAI Library, an open source library for causal analysis of time series and tabular data. The Salesforce CausalAI Library aims to provide a one-stop solution to the various needs in causal analysis including handling different data types, data generation, multi-processing for speed-up, utilizing domain knowledge

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 for time series forecasting tasks, these methods are not scalable for applications where data arrives sequentially

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

AUTHORS: Huan Wang, Aadyot Bhatnagar, Doyen Sahoo, Wenzhuo Yang, Steven Hoi, Caiming Xiong, Donald Rose TL;DR: Time series data is a critical source of insights for many applications, including IT Operations, Quality Management, Financial Analytics, and Inventory & Sales Management. While a variety of dedicated packages and software exist, engineers