LLM Copilot¶

This walkthrough covers the functime.llm module, which contains namespaced polars dataframe methods to interoperate Large Language Models (LLMs) with functime.

Let's use OpenAI's GPT models to analyze commodity price forecasts created by a functime forecaster. By default we use gpt-3.5-turbo.

Load data¶

In [ ]:

import os

from IPython.display import Markdown, display

os.environ["OPENAI_API_KEY"] = "..."  # Your API key here

import os from IPython.display import Markdown, display os.environ["OPENAI_API_KEY"] = "..." # Your API key here

In [ ]:

%%capture
import polars as pl

from functime.cross_validation import train_test_split
from functime.forecasting import knn

%%capture import polars as pl from functime.cross_validation import train_test_split from functime.forecasting import knn

In [ ]:

y = pl.read_parquet("../../data/commodities.parquet")
entity_col, time_col, target_col = y.columns
test_size = 30
freq = "1mo"
y_train, y_test = train_test_split(test_size)(y)
print("🎯 Target variable (y) -- train set:")
y_train.collect()

y = pl.read_parquet("../../data/commodities.parquet") entity_col, time_col, target_col = y.columns test_size = 30 freq = "1mo" y_train, y_test = train_test_split(test_size)(y) print("🎯 Target variable (y) -- train set:") y_train.collect()

We'll make a prediction using a knn forecaster.

In [ ]:

# Univariate time-series fit with automated lags
forecaster = knn(freq="1mo", lags=24)
forecaster.fit(y=y_train)
y_pred = forecaster.predict(fh=test_size)
y_pred.head()

# Univariate time-series fit with automated lags forecaster = knn(freq="1mo", lags=24) forecaster.fit(y=y_train) y_pred = forecaster.predict(fh=test_size) y_pred.head()

We'll also provide a short description of the dataset to aid the LLM in its analysis.

In [ ]:

dataset_context = (
    "This dataset comprises of forecasted commodity prices between 2020 to 2023."
)

dataset_context = ( "This dataset comprises of forecasted commodity prices between 2020 to 2023." )

Analyze Forecasts¶

Let's take a look at aluminum and European banana prices. You can select multiple (or just one) entity / time-series to analyze through the basket variable.

In [ ]:

analysis = y_pred.llm.analyze(
    context="This dataset comprises of forecasted commodity prices between 2020 to 2023.",
    basket=["Aluminum", "Banana, Europe"],
)
display(Markdown(analysis))

analysis = y_pred.llm.analyze( context="This dataset comprises of forecasted commodity prices between 2020 to 2023.", basket=["Aluminum", "Banana, Europe"], ) display(Markdown(analysis))

Compare Forecasts¶

Let's now compare the previous selection with a new one. We'll refer to these as baskets A and B.

In [ ]:

basket_a = ["Aluminum", "Banana, Europe"]
basket_b = ["Chicken", "Cocoa"]

basket_a = ["Aluminum", "Banana, Europe"] basket_b = ["Chicken", "Cocoa"]

Now compare!

In [ ]:

comparison = y_pred.llm.compare(basket=basket_a, other_basket=basket_b)
display(Markdown(comparison))

comparison = y_pred.llm.compare(basket=basket_a, other_basket=basket_b) display(Markdown(comparison))