Spectral Analysis
Find the hidden rhythms in your data. We expose the repeating cycles in your metrics — weekly surges, monthly dips, seasonal patterns — so you can time your decisions around them instead of being surprised by them.
Dataset
FMCG 2022-2024
Daily aggregated sales and quantity
1. Analysis Name
Spectral Analysis via FFT
We reveal the recurring cycles inside your data that aren't visible from a standard chart, so you can plan around patterns you know are real — not ones you're guessing at.
2. Problem Context
What you'll be able to decide
Is that monthly sales spike real or random noise? Is there a weekly rhythm you should be scheduling around? Spectral analysis separates genuine cycles from noise so you can act on patterns with confidence, not just intuition.
3. Time-Domain Representation
Observed series and detrended components
The original metric with its long-term trend, and the detrended version that isolates cyclical behavior.
Signal Statistics
4. Spectral Analysis Workflow
From time domain to frequency domain
The workflow detrends, windows, applies FFT, and identifies peaks to reveal dominant frequencies and their corresponding periods.
Detrend signal
Remove long-term trend to isolate cyclical components.
Apply windowing
Use Hann window to reduce spectral leakage at signal edges.
Compute FFT
Transform to frequency domain and compute power spectrum.
5. Frequency-Based Prediction
Signal reconstruction and 90-day forecast
The signal is reconstructed from the top 20 dominant FFT frequency components (dotted line). The forecast repeats the most recent annual cycle forward 90 days — showing what the series looks like if the observed seasonal pattern continues unchanged.
Spectral Summary
Top Frequencies
6. Dominant Periodicities
Characteristic time scales detected
Key Findings