Business Forecaster

Pro v1.0.0 1 view

Generate accurate time series forecasts from historical business data. Automatically detects trends and seasonality, selects optimal ARIMA/exponential smoothing models, and delivers confidence intervals with plain-language business insights.

What You Get

Get data-driven revenue and sales forecasts without statistical expertise or specialized software

The Problem

You need accurate forecasts for budgeting, capacity planning, and cash flow decisions, but lack access to statisticians or forecasting tools. Your historical data sits unused because time series forecasting seems too technical and complex.

The Solution

Business Forecaster automates the entire forecasting workflow by analyzing your historical time series data to detect trends, seasonality, volatility, and patterns. It validates data quality, detects and handles outliers through guided classification, and runs statistical tests including autocorrelation analysis and trend decomposition. The skill automatically selects the optimal forecasting method based on detected patterns - using Seasonal ARIMA for trend plus seasonality, standard ARIMA for trend-only data, Exponential Smoothing for strong seasonality, or simpler methods for stable data. It generates point forecasts with 80% and 95% confidence intervals, validates the forecast horizon, and backtests models on historical data with accuracy metrics. Results are translated into plain-language business insights with specific recommendations by role, risk assessments, monitoring plans, and professional visualizations.

How It Works

  1. 1 Upload and validate CSV time series data with date and value columns, checking for minimum 12 data points and calculating maximum reliable forecast horizon
  2. 2 Detect and handle outliers by asking user to classify anomalies as data errors, one-time events, or normal variation
  3. 3 Automatically detect patterns using statistical tests for trend direction, seasonality, volatility, and stationarity
  4. 4 Select optimal forecasting method based on detected patterns - Seasonal ARIMA, ARIMA, Exponential Smoothing, or Simple methods
  5. 5 Generate forecast with validated horizon, point estimates, confidence intervals, and accuracy metrics from backtesting
  6. 6 Create visualization showing historical data, forecasted values, and confidence bands with clear labels
  7. 7 Provide plain-language interpretation with pattern summary, forecast implications, risk factors, and monitoring recommendations

What You'll Need

  • CSV file with time series data including date column and value column
  • Minimum 12 data points, preferably 24+ for seasonality detection
  • Python 3.9+ with statsmodels, pandas, matplotlib, and numpy libraries
  • Consistent time intervals in data such as daily, weekly, monthly, or quarterly

Get This Skill

Requires Pro subscription ($9/month)

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