Forecasting inflation: The sum of the cycles outperforms the whole

Inflation dynamics reflect forces operating at different cycles, from short-lived shocks to longterm structural trends. We introduce the sum-of-the-cycles (SOC) method, which exploits this multifrequency structure of inflation for forecasting. SOC decomposes inflation into cyclical components, applies forecasting models suited to their persistence, and recombines them into an aggregate forecast. Across U.S. inflation measures and horizons, SOC consistently outperforms leading time-series benchmarks, reducing forecast errors by about 25 percent at short horizons and nearly 50 percent at long horizons. During the 2020-21 inflation surge, when many models - including advanced machine-learning methods - struggled, SOC retained strong performance by incorporating shortage indicators. Beyond accuracy, SOC enhances interpretability: financial variables dominate high- and business-cycle frequencies, Phillips Curve models are most informative at medium frequencies, and factor-based methods, forecast combinations, and shortage indices prevail at low frequencies. This combination of accuracy and transparency makes SOC a practical complement to existing tools for inflation forecasting and policy analysis.