Data Cleaning Is Your Secret Weapon Before Hitting Any Forecast Model

Most forecasting failures do not come from bad models — they come from dirty data. Before applying ARIMA, machine learning, or AI-based forecasting, your data must be trustworthy.

Why Data Cleaning Matters in Forecasting

• Missing timestamps break time-series continuity
• Outliers distort trends and seasonality
• Duplicate records inflate demand
• Inconsistent units create false signals

Common Data Issues in Excel & CSV Forecasting

Retail and wholesale data often contains gaps, mixed date formats, summary rows, or manual corrections. These issues silently reduce forecast accuracy.

How Clean Data Improves Forecast Accuracy

Proper cleaning ensures:

• Stable variance
• Reliable seasonality detection
• More accurate trend estimation

👉 This is why our AI Sales Forecast Tool performs automated data cleaning before generating forecasts.