Feed Gas Price Data Into Expense Forecasting

TL;DR — The Fuel Prices Scraper returns AAA's dated state fuel averages as JSON. Forecasts built on the current fix plus month/year-ago context stop being last quarter's number with a shrug attached.

Why expense forecasts quietly rot

Travel and mileage budgets are typically set quarterly — while fuel reprices daily. By mid-quarter, the variance is structural, not noise, and nobody can say when it started.

The fix is a feed, not a figure: dated state averages flowing into the forecast so every variance conversation has a timestamped market rate behind it.

How does this compare to the alternatives?

How to feed forecasts in 4 steps

How do I pull the forecast inputs?

import os
from apify_client import ApifyClient

client = ApifyClient(os.environ["APIFY_TOKEN"])
run = client.actor("thirdwatch/fuel-prices-scraper").call(
    run_input={"states": ["texas", "florida", "new-york", "illinois", "california"]}
)
rows = list(client.dataset(run["defaultDatasetId"]).iterate_items())

How do I derive the projection band?

import pandas as pd
df = pd.DataFrame(rows)
df["yoy_change"] = df.regular - df.year_ago
df["mom_change"] = df.regular - df.month_ago
print(df[["region", "regular", "mom_change", "yoy_change"]])

month_ago/year_ago give trend and seasonality context — the honest ingredients of a projection band.

How do I apply it to mileage spend?

monthly_miles = {"california": 4000, "texas": 6000}
forecast = sum(m / 25 * df.set_index("region").loc[s.title(), "regular"]
               for s, m in monthly_miles.items())  # 25 mpg fleet
print(f"Next-month fuel forecast: ${forecast:.0f}")

How do I archive for variance review?

Append each run's rows to a dated JSONL store — when actuals diverge, the benchmark series shows whether the market moved or the plan was wrong.

Sample output

{"region": "New York", "scope": "state:ny", "date": "2026-09-21",
 "regular": 3.48, "mid_grade": 3.82, "premium": 4.15,
 "diesel": 4.30, "e85": 2.98,
 "yesterday": 3.47, "week_ago": 3.44, "month_ago": 3.52, "year_ago": 3.36}

Common pitfalls

Forecasts need a refresh cadence — a feed that updates weekly beats a better model fed quarterly. State averages understate metro spend where most travel happens; pad dense-market states. Seasonal moves (summer blends, winter diesel) show in the year_ago comparison — read it before projecting. The actor supplies the market truth; policy and buffer are finance's call.

Related use cases

• Monitor fuel price trends for logistics budgets
• Compare state gas prices for travel planning
• Build a fuel cost model with AAA data
• Blog hub