cisco-tsm 1.0.2

Cisco Time Series Model is a pretrained Multi-resolution time series forecasting model.

Cisco Time Series Model 1.0

The Cisco Time Series Model is a foundation model trained to perform univariate zero-shot forecasting. Its core is a sequence of decoder-only transformer layers. It is architecturally inspired by the TimesFM2.0 model, with multiresolution modifications aimed at efficient use of long context. It expects a multiresolution context (x_c, x_f), where the resolution (i.e., space between data points) of x_c is 60 times the resolution of x_f. Both x_c and x_f can have length up to 512. The input contexts should be aligned “on the right,” e.g., if x_f consists of the 512 minutes terminating at 11:00AM on November 11, then x_c should consist of the 512 hours terminating at the same time. The output is a forecast of 128 points, which should be interpreted at the finer resolution; and corresponding quantiles for these points.

For convenience, we also provide utilities for preparing a multiresolution context from a single resolution context (with length up to 512 x 60 = 30,720) directly.

Latest Release (250M) - CTSM 1.0

Results and Improvements:

• Achieves state-of-the-art performance in the Observability (O11y) domain compared to leading models.
  • 1-minute resolution: Outperforms the second-best benchmarked model by 16.12% in MASE score.
  • 5-minute resolution: Outperforms the second-best benchmarked model by 12.42% in MASE score.
• Improves GIFT-EVAL (public) Benchmark MASE score by 8.57% compared to our previous release.
  

Key improvements over our November 2025 release (cisco-time-series-model-1.0-preview):

• Trained from scratch (NO continued pretraining (CPT) from TimesFM weights).
• Uses 2× more internal observability (O11y) data in the training mixture.
• Reduces size from ~500M → ~250M parameters.
• Adds Short-Context Training, Bidirectional Coarse Attention, and RoPE for better robustness overall.

Model Architecture and Training Details

Architecture diagram illustrating our novel additions.

Our latest Cisco Time Series Model 1.0 is a 250M parameter model that is trained from scratch and uses 25 transformer layers (vs 50 in 1.0-preview). This checkpoint leverages the architectural changes of expanded quantiles (15 quantile outputs, 0.01-0.99), rotary positional embeddings (RoPE), special token, separate resolution embeddings for each of coarse context / fine context / special token, and bidirectional attention over the coarse-resolution context.

Standard Causal Attention vs Bidirectional Coarse Attention (Ours):

A representation of bidirectional attention over the coarse context, where the model attends to both left and right context within the coarse context, while still attending causally to the fine context.

Example Visualization of Multiresolution Time Series Input to the Model

Multiresolution time series example with padded 1-hour context.

Training Datasets

The distribution of the training corpus is as follows:

• Internal Splunk Observability Cloud metric time series (upweighted ~2× vs the previous release) datasets:
  • (1-hour, 1-minute) resolution: 46.2%
  • (5-hour, 5-minute) resolution: 21.8%
• Public datasets:
  • GIFT-EVAL : 19.5%
  • Chronos : 3%
• Synthetic dataset: 9.5%

Technical Report

• A detailed technical report is available on arXiv (also available locally here).
  

NOTE: The report focuses on our November 2025 release (1.0-preview), so some training details differ from our latest release. An updated technical report with details on the latest release will be made available in the near future.

Benchmark Results

> Observability Data

With reference to the training and validation sets, these time series are both out-of-domain and in-the-future. We apply curation rules similar to those described in Section 3 (of the technical report) to ensure a diverse and high-quality benchmark.
We report metrics (models sorted by MASE scores, lower is better) on both 1-minute and 5-minute resolution data (with coarse contexts at 1-hour and 5-hour resolution, respectively). For observability data, error metrics are computed per horizon, then all horizons are aggregated via arithmetic mean. This quantity is normalized by a similar computation using a naive baseline which simply forecasts the final value in the context for all time steps in the horizon (as no natural seasonality is available).

An input context of 1024 length is used with a forecast horizon of 128 length for all models. Metrics of MSIS and CRPS are both computed using 9 quantiles (0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9) to keep them consistent with the GIFT-EVAL Benchmark.

1. 1-minute Resolution data

MODEL	MSE	MAE	MASE	sMAPE	MSIS	CRPS
CTSM-1.0 (Ours)	0.196	0.366	0.562	0.923	0.164	0.295
Toto_Open_Base_1.0	0.270	0.436	0.670	0.967	0.303	0.357
TimesFM-2.5	0.250	0.441	0.671	0.980	0.186	0.361
Chronos-2	0.242	0.421	0.674	0.955	0.192	0.341
TimesFM-2.0	0.338	0.547	0.762	1.030	0.214	0.443
Chronos-bolt-base	0.439	0.617	0.810	1.137	0.321	0.524

2. 5-minute Resolution data

MODEL	MSE	MAE	MASE	sMAPE	MSIS	CRPS
CTSM-1.0 (Ours)	0.232	0.425	0.543	1.005	0.195	0.340
Chronos-2	0.282	0.475	0.620	1.040	0.176	0.383
TimesFM-2.5	0.293	0.490	0.621	1.047	0.171	0.395
Toto_Open_Base_1.0	0.309	0.491	0.647	1.045	0.267	0.395
TimesFM-2.0	0.322	0.527	0.723	1.065	0.202	0.424
Chronos-bolt-base	0.386	0.567	0.737	1.114	0.290	0.481

> GIFT-EVAL Benchmark

All results below are computed on the entire GIFT-EVAL benchmark, with forecast horizon capped at ≤ 128. Models are sorted by MASE scores (lower is better). We adopt the standard approach of normalizing errors per dataset before applying a geometric mean.

1. Using the entire time series context

MODEL	MSE	MAE	MASE	sMAPE	MSIS	CRPS	Test Leak.
Chronos-2	0.493	0.674	0.692	0.904	0.485	0.542	NO
TimesFM-2.5	0.493	0.684	0.707	0.857	0.530	0.556	NO
CTSM-1.0 (Ours)	0.510	0.692	0.715	0.936	0.538	0.564	NO
TimesFM-2.0	0.532	0.704	0.730	0.950	0.563	0.573	YES
Toto_Open_Base_1.0	0.573	0.715	0.738	0.954	0.549	0.580	NO
Chronos-bolt-base	0.552	0.721	0.749	0.967	0.591	0.589	YES
CTSM-1.0-preview (Ours, prev.)	0.585	0.757	0.782	0.999	0.584	0.615	YES

2. Using the time series context truncated to 1024 points

MODEL	MSE	MAE	MASE	sMAPE	MSIS	CRPS	Test Leak.
Chronos-2	0.471	0.659	0.678	0.887	0.464	0.531	NO
TimesFM-2.5	0.464	0.667	0.684	0.851	0.494	0.540	NO
TimesFM-2.0	0.521	0.697	0.707	0.933	0.529	0.566	YES
Toto_Open_Base_1.0	0.528	0.691	0.715	0.932	0.526	0.559	NO
CTSM-1.0 (Ours)	0.510	0.692	0.715	0.936	0.538	0.564	NO
Chronos-bolt-base	0.562	0.722	0.747	0.967	0.597	0.592	YES
CTSM-1.0-preview (Ours, prev.)	0.585	0.757	0.782	0.999	0.584	0.615	YES

Test Leak.: YES indicates that the model's training data inadvertently includes portions of the GIFT-EVAL test set.

Usage notes

• If the input time series is missing some values, imputation via last value is recommended; if the time series is naturally sparse and this leads to excessive imputation (e.g., more than 30% of values are imputed), the model's forecasts will deteriorate.
• This release includes short-context training with 1/3rd of the training data sampled uniformly in the range of [10, 511] in the fine context. However, the model generally works better when more coarse resolution history is provided.

Dependencies and Installation

Setup Virtual Environment

Create a Python 3.11 (recommended) virtual environment and install the package using uv:

# Install uv if required
curl -LsSf https://astral.sh/uv/install.sh | sh

# Create a virtual environment
uv venv --python 3.11

# Activate the environment
source .venv/bin/activate

PyPI Package

The Cisco Time Series Model is available as a PyPI package named cisco-tsm. You can install it using pip:

uv pip install cisco-tsm

Manual Installation

1. Clone the repository:

   git clone https://github.com/splunk/cisco-time-series-model.git
   cd cisco-time-series-model
   

2. [Optional] Install your preferred torch backend based on your OS and accelerators (CPU, GPU, TPU or Apple Silicon):

   • Install PyTorch.

3. Install the package:

   uv pip install -e .
   

4. (Deprecated) If you want to use the 1.0-preview model, change directory to 1.0-preview/ and use the corresponding package (follow its README for installation and usage):

   cd 1.0-preview/
   

Example Code

import torch
import numpy as np
from cisco_tsm import CiscoTsmMR, TimesFmHparams, TimesFmCheckpoint

rng = np.random.default_rng(42)

## Sample data
T = 512 * 60
hours = (T + 59) // 60
k = np.arange(hours, dtype=np.float32)
h = (80 + 0.1 * k) * (1 + 0.25 * np.sin(2 * np.pi * k / 24))
t = np.arange(T, dtype=np.float32)

input_series_1 = h[(t // 60).astype(int)] * (1 + 0.05 * np.sin(2 * np.pi * t / 30)) + rng.normal(0, 0.4, size=T)

# Hyperparameters
hparams = TimesFmHparams(
    num_layers=25, # For `1.0-preview`, use `num_layers=50`.
    use_positional_embedding=False,
    backend="gpu" if torch.cuda.is_available() else "cpu",
    quantiles=[0.01, 0.05, 0.1, 0.2, 0.25, 0.3, 0.4, 0.5, 0.6, 0.7, 0.75, 0.8, 0.9, 0.95, 0.99]
)

ckpt = TimesFmCheckpoint(huggingface_repo_id="cisco-ai/cisco-time-series-model-1.0") # For `1.0-preview`, use `huggingface_repo_id="cisco-ai/cisco-time-series-model-1.0-preview"`.

model = CiscoTsmMR(hparams=hparams, checkpoint=ckpt)

# Model Inference
forecast_preds = model.forecast(input_series_1, horizon_len=128)

# Access forecast mean and quantiles of each series
mean_forecast = forecast_preds[0]['mean'] # (128,)
quantiles = forecast_preds[0]['quantiles'] # dict with keys as quantile levels (0.01, 0.05, ...., 0.95, 0.99) and values as (128,) numpy arrays

# You can also forecast multiple series at once
T = 25_000
hours = (T + 59) // 60
k = np.arange(hours, dtype=np.float32)
h = 120 / (1 + np.exp(-0.01 * (k - 300))) + 10 * np.cos(2 * np.pi * k / (24*7))
t = np.arange(T, dtype=np.float32)
input_series_2 = h[(t // 60).astype(int)] + 2 * np.sin(2 * np.pi * t / 60) + rng.normal(0, 0.5, size=T)

multi_series_forecasts = model.forecast([input_series_1, input_series_2], horizon_len=128)

# Long horizon forecasting is also supported and can be invoked as follows
long_horizon_forecasts = model.forecast(input_series_1, horizon_len=240)

Example Notebooks

We also provide a few Jupyter notebooks demonstrating how to use the Cisco Time Series Model for forecasting real-world time series data:

• CPU Utilization Forecasting
• Alerting Server Response Time Forecasting
• Internet Traffic Forecasting

Notebooks contributed by: Huaibo Zhao

Self-Hosting

The Cisco Deep Time Series Model (CDTSM) can be self-hosted as a FastAPI inference server, enabling on-premise or private-cloud deployments with full control over the runtime environment.

Quick start (Docker):

cd serve/
cp .env-example .env   # set CDTSM_AUTH_TOKEN (user-defined token for authentication)
docker compose up --build

Quick start (process-based):

cd serve/
cp .env-example .env   # set CDTSM_AUTH_TOKEN (user-defined token for authentication)
export CDTSM_AUTH_TOKEN=<your-token>

# CPU-Only
make install-dev && make model-up

# GPU-Accelerated
make install-dev-gpu && make model-up

The serve/ directory provides:

• Process-based hosting via a Makefile with CPU and GPU targets
• Docker-based hosting with CPU and GPU images, persistent model cache, and non-root container execution
• Bearer token authentication
• Health (/health) and readiness (/ready) probes
• An AITK-compatible JSON API at POST /cdtsm/v1/ai/infer

Both CPU and GPU (NVIDIA CUDA) backends are supported. See the serve/README.md for full setup instructions, environment variables, API reference, and example requests.

Self-Hosting contributed by: Udaya Prasad Vakalapudi

Citation

If you find Cisco Time Series Model useful for your research, please consider citing the associated technical report:

@misc{gou2025ciscotimeseriesmodel,
      title={Cisco Time Series Model Technical Report}, 
      author={Liang Gou and Archit Khare and Praneet Pabolu and Prachi Patel and Joseph Ross and Hercy Shen and Yuhan and Song and Jingze Sun and Kristal Curtis and Vedant Dharnidharka and Abhinav Mathur and Hao Yang},
      year={2025},
      eprint={2511.19841},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2511.19841}, 
}

Authors:

• Liang Gou *
• Archit Khare *
• Praneet Pabolu *
• Prachi Patel *
• Joseph Ross *
• Hercy Shen *‡
• Yuhan (Ellen) Song *
• Jingze Sun *
• Kristal Curtis †
• Vedant Dharnidharka †
• Abhinav Mathur †
• Hao Yang †

* These authors contributed equally to the core development of this work, listed alphabetically by last name.
† These authors contributed equally to supporting and extending this work, listed alphabetically by last name.
‡ Hercy Shen contributed to this work while an intern at Splunk.

License

This project is licensed under the Apache-2.0 License. See the LICENSE file for more details.