Summary of Local Vs. Global Models For Hierarchical Forecasting, by Zhao Yingjie et al.
Local vs. Global Models for Hierarchical Forecasting
by Zhao Yingjie, Mahdi Abolghasemi
First submitted to arxiv on: 10 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed study explores the impact of distinct information utilization on hierarchical time series forecasting, proposing and evaluating both local and global forecasting models. By developing Global Forecasting Models (GFMs) that exploit cross-series and cross-hierarchies information, the authors aim to improve forecasting performance and computational efficiency. The study employs reconciliation methods to ensure coherent forecasts and uses the Mean Absolute Scaled Error (MASE) and Multiple Comparisons with the Best (MCB) tests to assess statistical significance. The findings indicate that GFMs possess significant advantages for hierarchical forecasting, providing more accurate and computationally efficient solutions across different levels in a hierarchy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Hierarchical time series forecasting is important for making decisions in many areas, but it’s hard because it involves multiple levels of data and information. This study looks at how using different types of information affects the accuracy of these forecasts. The researchers propose new models that use information from multiple sources to improve forecasting performance and efficiency. They test their models on various datasets and show that they are more accurate and efficient than other methods, such as Exponential Smoothing (ES) and Autoregressive Integrated Moving Average (ARIMA). |
Keywords
» Artificial intelligence » Autoregressive » Time series
