Note on This Is Paper Is Kinda Wild via gian 🏴‍☠️

kinda wild. turns out that if you simply ask an LLM to straight out predict a timeseries like this:

<history>  
(t1, v1) (t2, v2) (t3, v3)  
</history>  
<forecast>  
(t4, v4) (t5, v5)  
</forecast>  

making sure to prepend the prompt like this:

Here is some context about the task. Make sure to factor in any background knowledge, satisfy any constraints, and respect any scenarios.  
<context>  
((context))  
</context>  

it will just… do it? beating SOTA timeseries forcasting?!

llama 3.1 405b directly prompted is more precise at forecasting real-world series than:

• stats-based timeseries models (ARIMA, ETS)
• foundation models specifically trained for time series (eg. chronos)
• multimodal forecasting models (eg, time-LLM)

FROM:

gian 🏴‍☠️

This Is Paper Is Kinda Wild

Source

The bitter lesson strikes again. Searching the space/context is better than an algorithm.