---
type: method
slug: time-series-forecast
title: "Build a simple time-series forecast"
category: "Science & Data Analysis"
subcategory: "Data analysis"
level: advanced
source: https://deepstoryresearch.com/method/time-series-forecast
publisher: Deepstory Research
---

> **How to use this file:** attach it to ChatGPT, Claude, Gemini or Copilot (or paste the prompt section), replace every [PLACEHOLDER] with your details, and add any data or Deepstory evidence files the method needs.

## When to use this method
- Projecting sales, prices, traffic or demand.

## Prompt

# Build a simple time-series forecast
Method by Deepstory Research: deepstoryresearch.com/method/time-series-forecast

You are a forecasting analyst. Follow the method below step by step. Goal: A baseline forecast with method comparison, backtest error and prediction intervals.

## My inputs
- Series: [SERIES]  (example: attach monthly data)
- Horizon: [HORIZON]  (example: 12 months)
- Tool: [TOOL]  (example: Python, Excel)

Before you start: if any input above is still in [BRACKETS] or missing, ask me for it in one short numbered list and wait. Do not guess. If I attached files (data, briefs, Deepstory evidence files), treat them as the primary source.

## Steps to follow
1. Plot and decompose trend and seasonality.
2. Fit naive, seasonal naive and exponential smoothing baselines.
3. Backtest and compare error.
4. Forecast with intervals.
5. Explain the drivers that could break the forecast.

## Deliver the output in this structure
- Decomposition
- Model comparison
- Forecast table with intervals
- Code
- Risks

## Quality rules
- Beat the naive baseline or say the model adds nothing.
- Label every number as FACT (with source and date), ESTIMATE (show the calculation) or ASSUMPTION.
- If a Deepstory evidence file is attached, use its figures as the reference values and cite its source references; tag claims that are not from it as [UNVERIFIED].
- End with: the 3 assumptions that matter most, what would change the answer, and the single best next step.

## Evidence you can attach
- Gold Price Fluctuation: https://deepstoryresearch.com/data/gold-price (AI context file: https://deepstoryresearch.com/context/gold-price.md)
- US Oil & Gas Dashboard: https://deepstoryresearch.com/data/us-oil-gas (AI context file: https://deepstoryresearch.com/context/us-oil-gas.md)
- US Inflation & the Fed: https://deepstoryresearch.com/data/us-inflation-fed (AI context file: https://deepstoryresearch.com/context/us-inflation-fed.md)
- Internet use ranking: https://deepstoryresearch.com/data/internet-use-peer-ranking

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Deepstory methods are process guidance for AI tools. Review AI output before relying on it; legal, tax, medical and investment matters need a qualified professional.