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FlaMinGo-timesfm

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metadata
library_name: transformers
license: apache-2.0
tags:
  - PartAI
  - time-series-forecasting
  - Classification
  - Foundation-Models
base_model:
  - google/timesfm-2.0-500m-pytorch

Model Detail

Shano is a foundation model for time-series classification, fine-tuned on extensive Persian financial datasets. Developed by Part AI, it extends Google's TimesFM model by adding classification capability, enabling it to handle both forecasting and classification tasks.

How to use

To perform inference on your dataset, follow these steps:

  1. Clone our repository
  2. Install the required dependencies. Ensure you are using Python 3.11:
cd Shano
pip install --upgrade pip
pip install timesfm[torch]
pip install -e .
  1. Download the TimeSFM model weights:

Configuration

Before running the model, set up the necessary configurations in the .env file located in the repository. The following environment variables must be configured:

  • MODEL_PATH: Path to the TimeSFM model
  • SAVE_MODEL_PATH: Path to save the fine-tuned model for the classification task
  • TRAIN_FILE_PATH: Path to the training dataset
  • TEST_FILE_PATH: Path to the test dataset
  • USE_GPU: Use GPU or CPU
  • NUM_LAYERS: Number of layers in the TimeSFM model (chosen based on the TimeSFM model size: 200M or 500M)
  • TARGET_COL_NAME: Target column index in the dataset

Example .env file:

MODEL_PATH=/app/weights/torch_model.ckpt
SAVE_MODEL_PATH=timesfm_classification_model.pth
TRAIN_FILE_PATH=/app/dataset/ECG200_TRAIN
TEST_FILE_PATH=/app/dataset/ECG200_TEST
USE_GPU=cpu
NUM_LAYERS=20
TARGET_COL_NAME=0

Fine-Tuning the Model

To fine-tune the TimeSFM model on your dataset, navigate to the experiments/torch_classification_timesfm directory in Shano repo and run the following command:

python run_timesfm_classification.py

This script will load the dataset, fine-tune the model, and save the fine-tuned model to the path specified in SAVE_MODEL_PATH.

Alternatively, you can use the notebooks/finetune_timesfm_torch_classification.ipynb Jupyter notebook to fine-tune the model and perform inference interactively.

Notes

  • Ensure that the dataset paths (TRAIN_FILE_PATH and TEST_FILE_PATH) are correctly specified in the .env file.
  • If using a GPU, set USE_GPU=gpu in the .env file and ensure that CUDA is properly installed.
  • The number of layers (NUM_LAYERS) should match the TimesFM size (200M or 500M).
  • Ensure that the training and testing datasets are in .csv format.

Evaluation

Shano, available in two versions (Shano-200M and Shano-500M), was evaluated against three popular time-series classification models (MOMENT, TimesNet, and GPT4TS) on multiple datasets from the UCR collection.

Dataset Shano (Timesfm-200M) Shano (Timesfm-500M) MOMENT TimesNet GPT4TS
Ham 69.5 63.8 58.1 68.6 78.1
InsectWingbeatSound 62.1 54.4 60.7 52.9 59.8
ECG5000 94 91.6 94.2 58.4 58.4
ECG200 90 87 76 83 79
BirdChicken 85 75 85 45 55
Adiac 62.9 22.3 68.8 56.5 59.8
ProximalPhalanxOutlineAgeGroup 82.4 80.5 86.3 86.8 83.9
Trace 92 76 100 76 71
wafer 99.5 98.5 99.7 98.9 99.4
CBF 99.4 ‍ 90.1 96 76.1 83
ElectricDevices 70 64 64.6 55.2 50.6
MoteStrain 80 74.6 77.4 70 68.1
TwoLeadECG 90.1 80.1 84.7 63.3 65.8
ShapeletSim 89.4 63.3 96.1 50 48.9
Cricket_Z 76.7 61.5 73.1 45.9 39.7
Strawberry 92.2 86.6 95.1 94.6 93.5
FordA 93.1 81.3 93.6 91.3 91.4
Symbols 87.6 81.8 93.6 86.4 69.4
MiddlePhalanxTW 63.7 63.4 53.2 50.6 57.1
yoga 77.5 73.6 83.4 67.2 69.1
Meat 93.3 93.3 91.7 43.3 66.7
MedicalImages 71.7 58.2 76.2 57.1 49.6
DistalPhalanxTW 77.5 77 61.2 60.4 61.9
ProximalPhalanxOutlineCorrect 81.1 79.7 85.6 86.9 80.1
Herring 59.4 59.4 59.4 53.1 57.8
ToeSegmentation2 86.2 73.1 91.5 73.1 73.1
OSULeaf 79 57 78.5 39.7 23.1

Our results show that Shano achieves competitive performance across diverse time-series classification tasks. In several cases, it outperforms existing models, while in most other cases, it delivers results comparable to the best-performing models. Since Shano has been fine-tuned on Persian financial data to improve forecasting capability, we also compared its forecasting performance against TimeSFM on crypto-related datasets we gathered. This evaluation provides insights into how fine-tuning affects the model's ability to predict financial trends.

Dataset MSE (pretrained) MAE (pretrained) MSE (fine-tuned) MAE (fine-tuned)
Crypto-1 1.93E-06 1.76E-04 1.24E-06 0.000144
Crypto-2 4.31E-06 2.43E-04 3.66E-06 0.000218
Crypto-3 7.36E-06 3.31E-04 6.54E-06 0.000307
Crypto-4 6.71 0.3 5.92 0.28
Crypto-5 7.26E-07 1.08E-04 6.68E-07 0.0001
Crypto-6 54028.91 29.82 40619.24 25.42
Crypto-7 0.19 0.0499 0.18 0.0458
Crypto-8 0.00181 0.00543 0.00168 0.00515

These results highlight the effectiveness of fine-tuning on Persian financial data, which enhances the model’s adaptability to structured time-series patterns.

Contact us

If you have any questions regarding this model, you can reach us via the community on Hugging Face.