Summary of Advancing Enterprise Spatio-temporal Forecasting Applications: Data Mining Meets Instruction Tuning Of Language Models For Multi-modal Time Series Analysis in Low-resource Settings, by Sagar Srinivas Sakhinana et al.
Advancing Enterprise Spatio-Temporal Forecasting Applications: Data Mining Meets Instruction Tuning of Language Models For Multi-modal Time Series Analysis in Low-Resource Settings
by Sagar Srinivas Sakhinana, Geethan Sannidhi, Chidaksh Ravuru, Venkataramana Runkana
First submitted to arxiv on: 24 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 This paper proposes a novel approach to spatio-temporal forecasting in transportation, logistics, and supply chain management. The method integrates strengths of traditional forecasting techniques with instruction tuning of small language models for trend analysis. It uses a mixture of experts (MoE) architecture with parameter-efficient fine-tuning (PEFT) methods, tailored for consumer hardware to balance performance and latency tradeoffs. The approach also leverages related past experiences to handle intra-series and inter-series dependencies in non-stationary data using a time-then-space modeling approach. The model predicts uncertainty to improve decision-making. This framework enables on-premises customization with reduced computational and memory demands, maintaining inference speed and data privacy/security. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about predicting where things will be and when. Right now, it’s hard to do this because we have a lot of complex data. The researchers came up with a new way to use computers to make better predictions. They combined two different methods that are good at different things. This helped them deal with big datasets and make more accurate forecasts. Their approach also helps predict when something might not go as planned, which is important for making good decisions. |
Keywords
* Artificial intelligence * Fine tuning * Inference * Instruction tuning * Mixture of experts * Parameter efficient
