See Where Your Revenue
Is Actually Heading
Spreadsheet trend lines miss seasonality, holidays, and trend breaks. MCP Analytics uses Prophet, ARIMA, and exponential smoothing to forecast your revenue with confidence intervals—so you plan on probabilities, not guesses.
Your Forecast Is Only as Good as Your Method
Most businesses forecast by extending a trend line. That works until it doesn't.
Seasonality Detection
Prophet and ARIMA automatically detect weekly, monthly, and annual seasonal patterns in your data. Your December forecast accounts for holiday spikes—your spreadsheet's linear trend doesn't.
Confidence Intervals
Every forecast includes 80% and 95% confidence bands. Plan your hiring, inventory, and cash flow for the expected case, but know the realistic range for worst-case and best-case scenarios.
Trend Change Detection
Prophet identifies changepoints where your growth rate shifted. Know exactly when your trajectory changed—was it a new product launch? A market shift? A pricing change?—and whether the new trend is sustainable.
Upload. Forecast. Plan.
Three steps from historical data to actionable projections
Export Your Revenue Data
Export historical revenue from your accounting software, e-commerce platform, or CRM as CSV. Include a date column and revenue column. 12+ months of history is recommended for accurate seasonal detection.
Upload Your CSV
Drop the file into MCP Analytics. The system detects date and revenue columns automatically and selects the best forecasting model—Prophet, ARIMA, or exponential smoothing—for your data pattern.
Get Your Forecast
Receive a forecast report with predicted values, confidence intervals, seasonal decomposition, trend analysis, and AI-written business commentary on what the numbers mean.
Forecasting Methods & Analyses
Multiple models for different data patterns, all in one platform
Prophet Forecasting
Facebook's time series model. Handles seasonality, holidays, and missing data gracefully. Best for data with strong seasonal patterns.
ARIMA
The statistical standard for time series. Auto-selects optimal parameters (p,d,q). Works well for stationary data with autocorrelation patterns.
Exponential Smoothing
Holt-Winters method. Captures level, trend, and seasonal components. Fast and interpretable for business planning.
Seasonal Decomposition
Separates your revenue into trend, seasonal, and residual components. Understand the structure of your data before forecasting.
Trend Analysis
Growth rates, changepoint detection, and momentum indicators. Is your growth accelerating, decelerating, or flat?
Anomaly Detection
Flags unusual revenue days or periods that deviate from expected patterns. Spot problems or opportunities early.
Cash Flow Forecast
Project future cash flows based on revenue patterns, accounting for payment timing and seasonal cash needs.
Demand Forecasting
Predict product-level or category-level demand. Essential for inventory planning and procurement decisions.
Multi-Model Comparison
Run Prophet, ARIMA, and ETS side by side. Compare accuracy metrics (MAE, RMSE, MAPE) and pick the model that fits best.
MCP Analytics vs Spreadsheet Forecasting
What you gain beyond a trend line
| Capability | MCP Analytics | Excel / Sheets |
|---|---|---|
| Prophet / ARIMA / Holt-Winters | ||
| Automatic seasonality detection | ||
| Confidence intervals (80% / 95%) | ||
| Trend changepoint detection | ||
| AI-written business commentary | ||
| Multi-model accuracy comparison | ||
| Linear trend line | ||
| Requires coding or formulas | No | Complex formulas |
Revenue Forecasting FAQ
How much historical data do I need for accurate forecasting?
For reliable forecasts, 12+ months of data is recommended. This gives the models enough history to detect seasonal patterns and trend changes. Prophet can work with as few as 6 months, but accuracy improves significantly with more data. For businesses with strong seasonality (retail, hospitality), 2+ years captures full annual cycles.
What forecasting methods does MCP Analytics use?
Three proven methods: Prophet (Facebook's time series model, excellent for data with strong seasonality and holidays), ARIMA (autoregressive integrated moving average, the statistical standard for time series), and Exponential Smoothing / Holt-Winters (captures level, trend, and seasonal components). The system selects the best model for your data pattern.
What are confidence intervals in a forecast?
Confidence intervals show the range where the actual value is likely to fall. A 95% confidence interval means there's a 95% probability the actual revenue will be within that range. Wider intervals mean more uncertainty. Reports include both 80% and 95% confidence intervals so you can plan for best-case, expected, and worst-case scenarios.
Can I forecast revenue for specific products or segments?
Yes. Upload a CSV with revenue broken out by product, category, region, or any segment you want to forecast separately. You can also run overall revenue forecasts on your total numbers. Each forecast produces its own interactive report with charts, decomposition, and recommendations.
How does this compare to forecasting in Excel or Google Sheets?
Spreadsheet forecasting typically uses simple trend lines or moving averages. MCP Analytics uses Prophet and ARIMA—statistical models that handle seasonality, holidays, trend changes, and missing data automatically. You also get confidence intervals (not just a single line), anomaly detection, seasonal decomposition, and AI-written business recommendations that spreadsheets can't provide.
Forecasting & Time Series Resources
Guides, tutorials, and deep-dives on revenue forecasting
Ready to Forecast with Confidence?
Upload your revenue CSV and get a forecast with confidence intervals in under 3 minutes. No credit card required.