mlboydaisuke/TimesFM-2.5-200M-CoreAI

TimesFM 2.5 200M — Core AI

google/timesfm-2.5-200m-transformers (Apache-2.0, 200M) converted to Apple Core AI .aimodel — the zoo's first time-series forecasting foundation model. A decoder-only patched transformer: feed it any univariate series, get a 128-step point + 10-quantile forecast, entirely on device.

TimesFM is a decoder-only transformer over time-series patches (32 points/patch), with the familiar LLM stack — RoPE, RMSNorm sandwich-norm, QK-norm, a learnable per-dim attention scale — but numeric patches in and quantile forecasts out. The zoo port runs it as **one stateless Core AI graph

• a host DSP wrapper** (RevIN normalization, flip-invariance, continuous-quantile head): no LLM runtime, just CoreAIKit's GraphModel.

Contents

• timesfm_2p5_200m_ctx2048_fp16.aimodel — the transformer graph (fp16, ~463 MB). Fixed context 2048 (64 patches); shorter series are front-padded + masked by the host, so one bundle covers every context length ≤ 2048. Inputs tok_in[1,64,64], cos/sin[1,64,80], attn_bias[1,1,64,64] → outputs proj_point[1,64,1280], proj_q[1,64,10240].
• host/ — the Python host-DSP reference (timesfm_core.py, host_forecast.py): patching, two-level RevIN (global + per-patch causal Welford), flip-invariance (2 graph calls on ±input), continuous-quantile head, denormalization, positivity clamp. This is the exact spec the Swift Forecaster follows.

Gates (vs the HF TimesFm2_5ModelForPrediction fp32 oracle)

• Re-authored graph vs HF projections: cos 1.0000000 (MAE ~1e-6).
• Independent host DSP + graph vs HF final forecast: cos 1.0000000 (rel ~1e-8).
• Core AI fp16 graph, Mac GPU: cos ≥ 0.99998; end-to-end forecast cos 0.9999999, values match HF to 2–3 decimals — including a front-padded short-context case.
• iPhone 17 Pro, in-app (KitForecaster, AOT h18p): device forecast == Mac to 3 decimals (Δ ≤ 0.001, fp16 GPU rounding).
• Mac GPU ~7 ms/graph → ~14 ms per 128-step forecast (flip = 2 calls); iPhone 17 Pro ~25 ms warm (54 ms cold). iOS h18p AOT: clean, device-verified.

Use (Python, Core AI runtime)

import numpy as np, torch, coreai.runtime as rt, asyncio
from host_forecast import forecast          # host/host_forecast.py
from timesfm_core import EngineCore          # thin engine adapter (see host/)

CFG = dict(patch=32, horizon=128, hidden=1280, layers=20, heads=16,
           head_dim=80, inter=1280, q=9, oql=1024, eps=1e-6)
model = asyncio.run(rt.AIModel.load("timesfm_2p5_200m_ctx2048_fp16.aimodel",
                                    rt.SpecializationOptions.from_preferred_compute_unit_kind(
                                        rt.ComputeUnitKind.gpu())))
core = EngineCore(model.load_function("main"), torch.float16)
series = torch.tensor(my_1d_series, dtype=torch.float32)     # any length ≤ 2048
mean_pred, full_pred = forecast(core, series, ctx_len=2048, cfg=CFG)   # (128,), (128,10)

Use (CoreAIKit, Swift)

let forecaster = try await KitForecaster(catalog: "timesfm-2.5-200m")
let out = try await forecaster.forecast(series)          // [Float] → point + quantiles
// out.mean (128-step), out.quantiles (128 × 10)

Base model: TimesFM 2.5 (Google Research). Core AI export: coreai-model-zoo. Apache-2.0.