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)
