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.
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.
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.
This article explores TimeMixer, a new time series forecasting model, and its implementation. The article delves into its inner workings and provides a benchmark comparison with other models.
A powerful library by Amazon — coding example included
