File size: 10,695 Bytes
51bb0b7 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 | """Synthetic prior v2 (Modular-TS v3): new generator families + augmentation cascade.
Motivated by the run2/run3 GIFT-Eval diagnosis (v3/INSIGHTS.md) and TempoPFN's
leave-one-out prior ablation (arXiv 2510.25502, Table 3: removing the regime-switching
OU/SDE prior costs the most, then CauKer, spikes, stochastic rhythms, step functions,
sawtooth; the augmentation cascade is worth ~5% CRPS):
New univariate families (all vectorised torch, run on the GPU):
* ou_regime - Ornstein-Uhlenbeck with Markov regime switching of mean / vol / speed
* waveform - non-sinusoidal periodic waves: sawtooth, square (random duty), triangle,
spike trains, with optional slow period drift (phase-drifting cycles)
* template - "load-curve" seasonality: a random smooth+spiky daily profile tiled at a
bank period, optionally modulated by a weekly profile (24x7, 96x7, 288x7)
and multiplicative trend; near-deterministic, low-noise (m4_hourly-like)
* steps - piecewise-constant levels with Poisson change points (+ small noise)
* growth - integrated / cumulative processes: cumsum of a positive rate (covid-like),
logistic / Gompertz growth with noise, compound-interest trends
Augmentations (applied after generation, each with its own probability):
time reversal, integration (cumsum), differencing, quantisation to a random grid,
censoring (clip at a random quantile), random smoothing conv filter, sample-and-hold
resampling artefacts, amplitude modulation, TSMixup of two series.
`SynthGen2.univariate` returns a mixture of the v1 prior (`SynthGen.univariate`) and the new
families; everything downstream (multivariatizers, covariates, masks) is inherited.
"""
from __future__ import annotations
import math
import torch
from torch import Tensor
from modular_ts.synth import PERIOD_BANK, SynthGen, SynthStream, _z
def _fft_filter(e: Tensor, phi: Tensor, L: int) -> Tensor:
"""y_t = sum_k phi^k e_{t-k} for each row (AR(1) filter via FFT), phi (n,)."""
n, T = e.shape
L = min(T, L)
k = phi.view(n, 1) ** torch.arange(L, device=e.device, dtype=torch.float32)
nfft = 1 << (T + L - 1).bit_length()
return torch.fft.irfft(torch.fft.rfft(e, nfft) * torch.fft.rfft(k, nfft), nfft)[:, :T]
class SynthGen2(SynthGen):
def __init__(self, *a, p_new: float = 0.55, p_aug: float = 0.5, **k):
super().__init__(*a, **k)
self.p_new, self.p_aug = p_new, p_aug
# ------------------------------------------------------------------ families
def _regimes(self, n, rate_lo=1e-4, rate_hi=3e-3):
"""Piecewise-constant regime index field (n,T) in [0,1) via Poisson change points."""
T = self.T
rate = self.logU(n, 1, lo=rate_lo, hi=rate_hi)
jumps = (self.U(n, T) < rate).float()
seg = torch.cumsum(jumps, -1).long() % 64 # segment id
table = self.U(n, 64)
return torch.gather(table, 1, seg)
def ou_regime(self, n):
r = self._regimes(n)
mu = (r - 0.5) * self.U(n, 1, lo=0.0, hi=6.0)
vol = torch.exp(self.U(n, 1, lo=-1.0, hi=1.0) * (self._regimes(n) - 0.5) * 2)
theta = self.logU(n, lo=1e-3, hi=0.3)
e = theta.view(n, 1) * mu + 0.3 * vol * self.N(n, self.T)
return _fft_filter(e, 1 - theta, 2048)
def _period(self, n):
idx = torch.randint(0, len(PERIOD_BANK), (n,), generator=self.g, device=self.dev)
per = PERIOD_BANK.to(self.dev)[idx]
rnd = self.logU(n, lo=3.0, hi=max(4.0, self.T / 4))
return torch.where(self.U(n) < 0.3, rnd, per)
def _phase(self, n, per):
"""Cycle phase in [0,1) with optional slow period drift (phase-drifting cycles)."""
drift = torch.where(self.U(n) < 0.3, self.N(n) * 0.02, torch.zeros(n, device=self.dev))
inst = 1.0 / per.view(n, 1) * (1 + drift.view(n, 1) * torch.tanh(self.rbf(n)))
return torch.frac(torch.cumsum(inst.expand(n, self.T), -1) + self.U(n, 1))
def waveform(self, n):
per = self._period(n)
ph = self._phase(n, per)
kind = torch.randint(0, 4, (n, 1), generator=self.g, device=self.dev)
duty = self.U(n, 1, lo=0.1, hi=0.9)
saw = ph * 2 - 1
sq = torch.where(ph < duty, 1.0, -1.0)
tri = 1 - 4 * (ph - 0.5).abs()
spk = torch.exp(-((ph - 0.5) ** 2) / (2 * self.logU(n, 1, lo=1e-4, hi=1e-2)))
w = torch.where(kind == 0, saw, torch.where(kind == 1, sq, torch.where(kind == 2, tri, spk)))
w = torch.where(self.U(n, 1) < 0.5, -w, w)
return w
def template(self, n):
"""Tiled random daily profile (smooth + bumps) x optional weekly profile."""
T = self.T
per = PERIOD_BANK.to(self.dev)[torch.randint(4, len(PERIOD_BANK), (n,), generator=self.g, device=self.dev)]
per = torch.where(self.U(n) < 0.6, torch.tensor([24.0, 96.0, 288.0, 48.0, 12.0, 7.0], device=self.dev)[
torch.randint(0, 6, (n,), generator=self.g, device=self.dev)], per)
ph = self._phase(n, per) # (n,T) in [0,1)
# profile = sum of K random bumps on the circle + low-order Fourier terms
K = 4
c = self.U(n, K, 1)
wdt = self.logU(n, K, 1, lo=0.02, hi=0.25)
a = self.N(n, K, 1).abs() * torch.where(self.U(n, K, 1) < 0.2, -1.0, 1.0)
d = torch.minimum((ph[:, None] - c).abs(), 1 - (ph[:, None] - c).abs())
prof = (a * torch.exp(-0.5 * (d / wdt) ** 2)).sum(1)
h = torch.arange(1, 4, device=self.dev, dtype=torch.float32).view(1, 3, 1)
prof = prof + (0.3 * self.N(n, 3, 1) / h * torch.sin(2 * math.pi * h * ph[:, None] + self.U(n, 3, 1, hi=6.3))).sum(1)
# weekly modulation for intraday periods
wk = self.U(n, 7, lo=0.5, hi=1.2)
wk = torch.where(self.U(n, 1) < 0.5, wk, torch.ones_like(wk))
day = (torch.cumsum((ph[:, 1:] < ph[:, :-1]).float(), -1)).long() % 7
day = torch.cat([torch.zeros(n, 1, dtype=torch.long, device=self.dev), day], -1)
mod = torch.gather(wk, 1, day)
sig = prof * mod
# slowly varying level and tiny noise (near-deterministic series)
sig = sig + self.B(0.5, n, 1) * 0.3 * _z(self.trend(n)) + self.logU(n, 1, lo=1e-3, hi=0.2) * self.N(n, T)
return sig
def steps(self, n):
r = self._regimes(n, 2e-4, 1e-2)
lv = torch.where(self.U(n, 1) < 0.5, torch.round(r * self.U(n, 1, lo=2, hi=20)), r)
return lv + self.logU(n, 1, lo=1e-3, hi=0.1) * self.N(n, self.T)
def growth(self, n):
T = self.T
tt = self.t / T
kind = torch.randint(0, 3, (n, 1), generator=self.g, device=self.dev)
# cumulative counts: cumsum of positive, slowly varying rate (+ noise)
rate = torch.exp(0.8 * _z(self.rbf(n)).clamp(-3, 3)) * (1 + 0.3 * self.N(n, T).abs() * self.B(0.5, n, 1))
cum = torch.cumsum(rate, -1)
# logistic / gompertz
t0 = self.U(n, 1, lo=-0.2, hi=1.2)
k = self.logU(n, 1, lo=2.0, hi=30.0)
logi = torch.sigmoid(k * (tt - t0))
gomp = torch.exp(-torch.exp(-k * (tt - t0)))
sig = torch.where(kind == 0, cum, torch.where(kind == 1, logi, gomp))
sig = sig * (1 + self.logU(n, 1, lo=1e-3, hi=0.05) * self.N(n, T))
return sig
# ------------------------------------------------------------------ augmentations
def augment(self, x: Tensor) -> Tensor:
n, T = x.shape
on = lambda p: self.U(n, 1) < p * self.p_aug * 2 # noqa: E731
# time reversal
x = torch.where(on(0.15), x.flip(-1), x)
# integration / differencing
xi = torch.cumsum(x - x.mean(-1, keepdim=True), -1)
x = torch.where(on(0.06), xi, x)
xd = torch.cat([x[:, :1] * 0, x[:, 1:] - x[:, :-1]], -1)
x = torch.where(on(0.04), xd, x)
# amplitude modulation
x = torch.where(on(0.1), x * (1 + 0.5 * torch.tanh(self.rbf(n))), x)
# random smoothing conv filter (moving average of random width via cumsum trick)
w = torch.randint(2, 16, (n, 1), generator=self.g, device=self.dev)
cs = torch.cumsum(torch.nn.functional.pad(x, (1, 0)), -1)
idx = torch.arange(1, T + 1, device=self.dev).view(1, T)
lo = (idx - w).clamp_min(0)
ma = (cs[:, 1:] - torch.gather(cs, 1, lo)) / (idx - lo).float()
x = torch.where(on(0.1), ma, x)
# sample-and-hold resampling artefacts
k = torch.randint(2, 8, (n, 1), generator=self.g, device=self.dev)
hold = torch.gather(x, 1, (torch.arange(T, device=self.dev).view(1, T) // k) * k)
x = torch.where(on(0.05), hold, x)
# TSMixup with a rolled partner
lam = self.U(n, 1, lo=0.2, hi=0.8)
xm = lam * _z(x) + (1 - lam) * _z(x.roll(1, 0))
x = torch.where(on(0.1), xm, x)
# censoring at a random quantile (one side)
qv = torch.quantile(x[:, :: max(1, T // 512)], 0.8 + 0.19 * torch.rand(1, device=self.dev).item(), dim=-1, keepdim=True)
x = torch.where(on(0.05), torch.minimum(x, qv), x)
return x
def quantize(self, x: Tensor) -> Tensor:
"""Round some series to a coarse grid (integer-valued / low-resolution sensors)."""
n = x.shape[0]
step = x.std(-1, keepdim=True).clamp_min(1e-6) * self.logU(n, 1, lo=0.02, hi=0.5)
q = torch.round(x / step) * step
return torch.where(self.U(n, 1) < 0.08, q, x)
# ------------------------------------------------------------------ mixture
def univariate(self, n) -> Tensor:
base = super().univariate(n)
fam = torch.randint(0, 5, (n, 1), generator=self.g, device=self.dev)
new = torch.zeros(n, self.T, device=self.dev)
gens = (self.ou_regime, self.waveform, self.template, self.steps, self.growth)
for i, g in enumerate(gens):
new = torch.where(fam == i, _z(g(n)), new)
# optional combination of the new family with a v1 component (additive)
comb = self.U(n, 1) < 0.35
new = torch.where(comb, new + self.logU(n, 1, lo=0.05, hi=1.0) * _z(base), new)
use_new = self.U(n, 1) < self.p_new
x = torch.where(use_new, new, base)
x = self.augment(x)
x = torch.nan_to_num(x, nan=0.0, posinf=0.0, neginf=0.0)
return x
def affine(self, x: Tensor) -> Tensor:
return self.quantize(super().affine(x))
class SynthStream2(SynthStream):
def __init__(self, T, tokens_per_batch, device="cpu", seed=0, patch=32, block=32, min_ctx=4, chunk=8, p_new=0.55):
super().__init__(T, tokens_per_batch, device, seed, patch, block, min_ctx, chunk)
self.gen = SynthGen2(T, device, seed, p_new=p_new)
|