Forecasting
54 bites tagged Forecasting — interview questions with model answers, and 60-second explainers.
Exponential Smoothing: Weighting Recent Data More Heavily
Exponential smoothing creates forecasts from a weighted average of past data, where weights decay exponentially for older points. It's used for quick, reliable predictions like inventory demand. The footgun: simple versions ignore trends and seasonality.
Stationarity: A Time Series's Stable Personality
A time series is stationary if its statistical personality, like its average and spread, remains constant over time. Many forecasting models require this stability to work correctly.
Time Series Decomposition: Separating Signal from Noise
Time series decomposition breaks a metric into its core parts: long-term trend, repeating seasonal patterns, and random noise. This helps you understand *why* a metric changed—was it a real shift or just the usual holiday rush?
Cycle Time Scatterplot: Visualize Your Team's Predictability
A Cycle Time Scatterplot visualizes delivery speed by plotting how long each task took against its completion date. Agile teams use it to spot predictability trends and set realistic delivery forecasts.
Monte Carlo Forecasting: Probabilities, Not Promises
Monte Carlo forecasting trades a single due date for a set of probabilities. By running thousands of random simulations based on past performance, it generates a range of likely completion dates, not a single guess.
Actionable Agile Metrics: Predicting 'Done'
Stop guessing 'done' and start forecasting with data. Actionable Agile Metrics use historical flow data—like cycle time and throughput—to answer 'When will it be done?' for customers who need predictability.
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