WaveFM: A Wavelet-Native Pretrained Model for Industrial Forecasting

Time-series foundation models have largely converged on a recipe of fixed-length time-domain patches and parameter counts in the hundreds of millions. On industrial telemetry, which is non-stationary and dominated by transients and harmonics, patching averages together the high-frequency structure that drives operational decisions. We introduce WaveFM, a 10M-parameter pretrained industrial forecasting model that replaces time-domain patching with tokens over a 5-level discrete wavelet decomposition, exposing six dyadic frequency bands to band-specific processing and output heads. A latent bottleneck shortens the cross-channel sequence from $97C$ to $25C$ tokens ($\approx 15\times$ fewer token-pair interactions). On 15 industrial and public datasets, zero-shot WaveFM achieves accuracy comparable to Chronos-2-Full (0.301 vs. 0.302 MSE; geometric-mean relative MSE 0.995, 95% CI [0.983, 1.008]), including on the seven public datasets alone (0.323 vs. 0.322), with one-twelfth the parameters and $13.8\times$ faster multivariate inference (2.5 ms per window). Same-corpus, parameter-matched controls show that pretraining data alone does not reproduce this result: PatchTST trained on the same corpus reaches 0.353. On high-band-energy windows and around detected transients, WaveFM's error is 6.2% and 7.6% lower than Chronos-2-Full's, with the largest gains in the finest detail bands. WaveFM trails Chronos-2-Full on a GIFT-Eval tier outside its pretraining domain (0.319 vs. 0.302) and at longer horizons.