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timessm-10m

Time Series Forecasting
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timessm
time-series
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gift-eval
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metadata 
license: apache-2.0
library_name: timessm
pipeline_tag: time-series-forecasting
tags:
  - time-series
  - forecasting
  - zero-shot
  - state-space-model
  - gift-eval
model-index:
  - name: TimeSSM-10M
    results:
      - task:
          type: time-series-forecasting
        dataset:
          name: GIFT-Eval (97 tasks)
          type: Salesforce/GiftEval
        metrics:
          - type: mase
            name: MASE (normalised by Seasonal Naive)
            value: 0.6948
          - type: crps
            name: CRPS (normalised by Seasonal Naive)
            value: 0.4763

TimeSSM: a 10M-parameter forecaster that chooses its sampling rate

TimeSSM-10M

A zero-shot time series forecaster of 9.98 million parameters. Its trunk interleaves diagonal linear state space layers (S4D) with attention over patches of 16 steps, reads up to 4096 steps and outputs nine quantiles for any horizon in one pass. The sampling rate is part of the model: a scalar tells the trunk which rate it is reading, and the RateIN layer, which has no learned weights, chooses at inference the rates worth reading from the past of the series.

It was trained from scratch in 31 hours on one A100, by one person, outside of any lab or company. Technical report: 10.5281/zenodo.24202829.

GIFT-Eval

97 configurations, zero-shot. MASE and CRPS are geometric means of the ratio to the official seasonal naive, as on the leaderboard. Lower is better.

MASE CRPS
TimeSSM-10M 0.6948 0.4763

The 80 % interval covers 79.3 % of the targets. Every series found in GIFT-Eval was removed from the training corpus, by name and by value. The model and its settings were selected on held-out subsets of the corpus, never on GIFT-Eval.

Under 10 million parameters

Entries of public size in the official results folder on 12 November 2026, recomputed from their per-configuration results.

Model Parameters CRPS MASE Availability
PDM-Femto 6M 0.4676 0.6843 API only
TimeSSM-10M 9.98M 0.4763 0.6948 Apache-2.0
Timestral-5M 5M 0.4789 0.7049 weights, modified MIT
FlowState-9.1M 9.1M 0.5019 0.7262 open weights
TimeSSM-2.5M 2.5M 0.5128 0.7544 Apache-2.0
TTM-R3 1.4M 0.5195 0.7240 open weights
Toto-2.0-4m 4M 0.5242 0.7565 open weights

TTM-R3 saw GIFT-Eval data in pretraining; 1.4M is the size of its main checkpoint.

GIFT-Eval CRPS against the number of parameters: TimeSSM-10M and TimeSSM-2.5M among the entries of the official results folder

CRPS against model size, for the entries of the official folder with a public parameter count.

Larger models, for scale

Model Parameters CRPS MASE
Timestral-28M 28M 0.4610 0.6780
t0-beta 256M 0.4738 0.6865
Toto-2.0-2.5B 2.5B 0.4759 0.6956
TiRex-2-Zeroshot 82.5M 0.4781 0.6973
Chronos-2 120M 0.4854 0.6978
TimesFM-2.5 200M 0.4903 0.7050

By sampling rate

Rate Configurations CRPS
10 seconds 6 0.547
5 to 15 minutes 30 0.468
hourly 31 0.426
daily 15 0.422
weekly 8 0.620
monthly 5 0.699
quarterly 1 0.736
yearly 1 0.788

These are ratios to the seasonal naive: by that measure the model does best on daily and hourly series and worst on monthly, quarterly and yearly ones. Compared with other models of its size, it is level with the best on series sampled every 5 to 15 minutes and behind them on daily and weekly series.

97 configurations
Configuration MASE CRPS
bitbrains_fast_storage/H/short 0.764 0.627
solar/H/short 0.884 0.526
solar/H/medium 0.932 0.341
solar/H/long 0.851 0.299
m_dense/D/short 0.386 0.269
ett2/H/short 0.791 0.708
ett2/H/medium 0.845 0.547
ett2/H/long 0.933 0.493
ett2/D/short 0.985 0.650
electricity/15T/short 0.583 0.515
electricity/15T/medium 0.715 0.646
electricity/15T/long 0.742 0.639
bitbrains_rnd/H/short 0.988 0.502
loop_seattle/H/short 0.623 0.537
loop_seattle/H/medium 0.578 0.362
loop_seattle/H/long 0.545 0.306
solar/W/short 0.919 0.873
bizitobs_application/10S/short 0.456 0.285
bizitobs_application/10S/medium 0.790 0.770
bizitobs_application/10S/long 0.942 1.087
bizitobs_service/10S/short 0.615 0.271
bizitobs_service/10S/medium 0.779 0.438
bizitobs_service/10S/long 0.943 0.945
bizitobs_l2c/5T/short 0.269 0.268
bizitobs_l2c/5T/medium 0.362 0.357
bizitobs_l2c/5T/long 0.315 0.307
ett2/W/short 0.870 0.751
loop_seattle/5T/short 0.733 0.595
loop_seattle/5T/medium 0.622 0.539
loop_seattle/5T/long 0.603 0.526
loop_seattle/D/short 0.513 0.418
bitbrains_rnd/5T/short 0.877 0.367
bitbrains_rnd/5T/medium 0.992 0.526
bitbrains_rnd/5T/long 0.978 0.517
electricity/W/short 0.676 0.479
solar/D/short 0.858 0.533
ett1/H/short 0.839 0.744
ett1/H/medium 0.835 0.610
ett1/H/long 0.950 0.597
temperature_rain/D/short 0.671 0.437
saugeen/W/short 0.593 0.480
covid_deaths/D/short 0.670 0.293
us_births/D/short 0.196 0.159
m4_weekly/W/short 0.737 0.593
car_parts/M/short 0.725 0.576
sz_taxi/H/short 0.767 0.640
ett1/15T/short 0.724 0.648
ett1/15T/medium 0.834 0.712
ett1/15T/long 0.833 0.666
saugeen/M/short 0.729 0.634
us_births/M/short 0.845 0.780
m4_quarterly/Q/short 0.702 0.736
ett1/W/short 0.917 0.904
saugeen/D/short 0.795 0.551
electricity/H/short 0.691 0.621
electricity/H/medium 0.785 0.611
electricity/H/long 0.780 0.562
ett1/D/short 0.953 0.689
bizitobs_l2c/H/short 0.381 0.371
bizitobs_l2c/H/medium 0.314 0.245
bizitobs_l2c/H/long 0.386 0.264
hospital/M/short 0.813 0.797
m4_daily/D/short 0.981 0.861
bitbrains_fast_storage/5T/short 0.646 0.305
bitbrains_fast_storage/5T/medium 0.865 0.539
bitbrains_fast_storage/5T/long 0.825 0.580
ett2/15T/short 0.673 0.643
ett2/15T/medium 0.787 0.674
ett2/15T/long 0.845 0.659
jena_weather/10T/short 0.362 0.177
jena_weather/10T/medium 0.809 0.224
jena_weather/10T/long 0.820 0.206
jena_weather/D/short 0.740 0.240
us_births/W/short 0.678 0.640
m_dense/H/short 0.528 0.471
m_dense/H/medium 0.435 0.300
m_dense/H/long 0.465 0.270
kdd_cup_2018/D/short 0.789 0.555
electricity/D/short 0.710 0.536
restaurant/D/short 0.689 0.384
jena_weather/H/short 0.733 0.273
jena_weather/H/medium 0.888 0.154
jena_weather/H/long 0.668 0.131
m4_monthly/M/short 0.718 0.735
hierarchical_sales/D/short 0.666 0.335
m4_hourly/H/short 0.601 0.518
m4_yearly/A/short 0.781 0.788
hierarchical_sales/W/short 0.706 0.423
kdd_cup_2018/H/short 0.708 0.692
kdd_cup_2018/H/medium 0.735 0.555
kdd_cup_2018/H/long 0.760 0.469
sz_taxi/15T/short 0.711 0.651
sz_taxi/15T/medium 0.757 0.546
sz_taxi/15T/long 0.750 0.479
solar/10T/short 0.780 0.508
solar/10T/medium 0.865 0.466
solar/10T/long 0.907 0.439

Model

TimeSSM-10M: the RateIN layer resamples the context to several rates and sign-flips it, the trunk (causal normalization, patches of 16 steps, four layers of gated S4D and causal attention run twice, quantile head) forecasts each one, and the RateIN layer returns the forecasts to the native grid and mixes them

The RateIN layer is the first and the last layer of the model. The trunk holds the 9.98M learned weights and runs once per rate and per sign.

Four layers, each a causal S4D layer followed by causal attention over the patch tokens, applied in two shared passes; width 352, state size 32, 8 heads; 9,982,512 parameters. Each series is normalized causally, patch by patch, then passed through an arcsinh. The horizon is read from learned future tokens, so there is no autoregressive loop. A scalar w scales the time step of the state space layers and the positions of the attention: w = 24 / season, with season the number of steps in a cycle of the declared frequency.

RateIN (paper) reads the context at several rates (coarser by 2 to 48, finer by 2 to 4), scores each on a causal backtest over the past of the series, and mixes the forecasts of the best rates, with sign-flip averaging. It learns nothing and fits nothing on the evaluation data.

Training: pinball loss on nine quantiles, on every context token as well as on the horizon; 13.3 billion points of public and synthetic series; 200,000 steps with a constant learning rate then a linear decay over the last 40,000.

Inference 

pip install git+https://github.com/IUseAMouse/TimeSSM.git@timessm-10m

import numpy as np
import torch
from timessm import from_pretrained, delta_scale

model = from_pretrained("IUseAMouse/timessm-10m")   # or a local directory; device="cuda" to run there

series = np.sin(np.arange(1024) * 2 * np.pi / 24)   # your history: raw values, any scale, NaN allowed
context = torch.tensor(series, dtype=torch.float32).unsqueeze(0)   # [1, length]
w = delta_scale(24, *model.delta_range)   # season length in steps: 24 for hourly data with a daily cycle
with torch.no_grad():
    out = model.forecast(context, 48, w=w)
quantiles = out["quantiles_denorm"][0]   # [48, 9], levels 0.1 to 0.9; column 4 is the median

This call runs the trunk at one rate. The full model, with the RateIN layer, runs through the evaluation harness (next section).

Reproduction 

git clone -b timessm-10m https://github.com/IUseAMouse/TimeJEPA && git clone -b timessm-10m https://github.com/IUseAMouse/TimeSSM
cd TimeJEPA && make gift-download && cd ../TimeSSM && uv sync --extra train && source .venv/bin/activate
hf download IUseAMouse/timessm-10m timessm_10m.ckpt --local-dir .
EVAL_CONFIG=timessm_10m_gift scripts/eval_ssm.sh timessm_10m.ckpt     # MASE 0.6948, CRPS 0.4763

These commands reproduce the number above from the released weights; the last one takes about four hours on one A100. The tag timessm-10m of both repositories is the code that produced it. configs/timessm_10m_gift.yaml carries every inference setting. The harness writes all_results.csv in the leaderboard's format and recomputes the metrics from the raw data with the official seasonal-naive denominators.

Files and license

  • model.safetensors and config.json, read by timessm.from_pretrained; timessm_10m.ckpt, the same weights in the format of the harness.
  • Code: TimeSSM (Apache-2.0) and TimeJEPA (harness, MIT).
  • Weights: Apache-2.0.

Citation 

@misc{vincent2026timessm10m,
  author    = {Vincent, Yvann},
  title     = {TimeSSM-10M: A Small Forecaster That Chooses Its Sampling Rate},
  year      = {2026},
  publisher = {Zenodo},
  doi       = {10.5281/zenodo.24202829},
  url       = {https://doi.org/10.5281/zenodo.24202829}
}

@misc{vincent_2026_23868904,
  author    = {Vincent, Yvann},
  title     = {RateIN: Time Series Foundation Models Have a Sampling Problem},
  year      = {2026},
  publisher = {Zenodo},
  doi       = {10.5281/zenodo.23868904}
}