README.md

---
license: mit
datasets:
- AutonLab/Timeseries-PILE
metrics:
- accuracy
- mse
- mae
- f1
tags:
- time series
- forecasting
- classification
- anomaly detection
- imputation
- transformers
- pretrained models
- foundation models
- time-series
---
# MOMENT-Large

MOMENT is a family of foundation models for general-purpose time-series analysis. The models in this family (1) serve as a building block for diverse **time-series analysis tasks** (e.g., forecasting, classification, anomaly detection, and imputation, etc.), (2) are effective **out-of-the-box**, i.e., with no (or few) task-specific exemplars (enabling e.g., zero-shot forecasting, few-shot classification, etc.), and (3) are **tunable** using in-distribution and task-specific data to improve performance. 

For details on MOMENT models, training data, and experimental results, please refer to the paper [MOMENT: A Family of Open Time-series Foundation Models](https://arxiv.org/pdf/2402.03885.pdf).

# Usage

Install the package using:
```bash
pip install git+https://github.com/moment-timeseries-foundation-model/moment-test.git
```

To load the pre-trained model for one of the tasks, use one of the following code snippets:

**Forecasting**
```python
from moment import MOMENTPipeline

model = MOMENTPipeline.from_pretrained(
    "AutonLab/MOMENT-1-large", 
    model_kwargs={
        'task_name': 'forecasting',
        'forecast_horizon': 96
    },
)
model.init()
```

**Classification**
```python
from moment import MOMENTPipeline

model = MOMENTPipeline.from_pretrained(
    "AutonLab/MOMENT-1-large", 
    model_kwargs={
        'task_name': 'classification',
        'n_channels': 1,
        'num_class': 2
    },
)
model.init()
```

**Anomaly Detection/Imputation/Pre-training**
```python
from moment import MOMENTPipeline

model = MOMENTPipeline.from_pretrained(
    "AutonLab/MOMENT-1-large", 
    model_kwargs={"task_name": "reconstruction"},
)
mode.init()
```

**Embedding**
```python
from moment import MOMENTPipeline

model = MOMENTPipeline.from_pretrained(
    "AutonLab/MOMENT-1-large", 
    model_kwargs={'task_name': 'embedding'},
)
```

## Model Details

### Model Description

- **Developed by:** [Auton Lab](https://autonlab.org/), [Carnegie Mellon University](https://www.cmu.edu/) and [University of Pennsylvania](https://www.upenn.edu/)
- **Funded by [optional]:** [More Information Needed]
- **Model type:** Time-series Foundation Model
- **License:** MIT License

### Model Sources

<!-- Provide the basic links for the model. -->

- **Repository:** https://github.com/moment-timeseries-foundation-model/
- **Paper:** https://arxiv.org/abs/2402.03885
- **Demo:** https://github.com/moment-timeseries-foundation-model/

## Uses

<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->

### Direct Use

<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->

[More Information Needed]

### Downstream Use [optional]

<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->

[More Information Needed]

### Out-of-Scope Use

<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->

[More Information Needed]

## Bias, Risks, and Limitations

<!-- This section is meant to convey both technical and sociotechnical limitations. -->

[More Information Needed]

### Recommendations

<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

## How to Get Started with the Model

Use the code below to get started with the model.

[More Information Needed]

## Training Details

### Training Data

<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->

[More Information Needed]

### Training Procedure

<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->

#### Preprocessing [optional]

[More Information Needed]


#### Training Hyperparameters

- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->

#### Speeds, Sizes, Times [optional]

<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->

[More Information Needed]

## Evaluation

<!-- This section describes the evaluation protocols and provides the results. -->

### Testing Data, Factors & Metrics

#### Testing Data

<!-- This should link to a Dataset Card if possible. -->

[More Information Needed]

#### Factors

<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->

[More Information Needed]

#### Metrics

<!-- These are the evaluation metrics being used, ideally with a description of why. -->

[More Information Needed]

### Results

[More Information Needed]

#### Summary



## Model Examination [optional]

<!-- Relevant interpretability work for the model goes here -->

[More Information Needed]

## Environmental Impact

<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->

We train multiple models over many days resulting in significant energy usage and a sizeable carbon footprint. However, we hope that releasing our models will ensure that future time-series modeling efforts are quicker and more efficient, resulting in lower carbon emissions.

We use the Total Graphics Power (TGP) to calculate the total power consumed for training MOMENT models, although the total power consumed by the GPU will likely vary a little based on the GPU utilization while training our model. Our calculations do not account for power demands from other sources of our compute. We use 336.566 Kg C02/MWH as the standard value of CO2 emission per megawatt hour of energy consumed for [Pittsburgh](https://emissionsindex.org/).

- **Hardware Type:** NVIDIA RTX A6000 GPU
- **GPU Hours:** 404
- **Compute Region:** Pittsburgh, USA
- **Carbon Emission (tCO2eq):** 

## Technical Specifications [optional]

### Model Architecture and Objective

[More Information Needed]

### Compute Infrastructure

#### Hardware

All models were trained and evaluated on a computing cluster consisting of 128 AMD EPYC 7502 CPUs, 503 GB of RAM, and 8 NVIDIA RTX A6000 GPUs each with 49 GiB RAM. All MOMENT variants were trained on a single A6000 GPU (with any data or model parallelism).

#### Software



## Citation [optional]

<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->

**BibTeX:**
If you use MOMENT please cite our paper: 

```bibtex

    @article{
        goswami2024moment,
        title={{MOMENT: A Family of Open Time-series Foundation Models}},
        author={Goswami, Mononito and Szafer, Konrad and Choudhry, Arjun and Cai, Yifu and Li, Shuo and Dubrawski, Artur},
        journal={arXiv preprint arXiv:2402.03885},
        year={2024},
    }
```


**APA:**

Goswami, M., Szafer, K., Choudhry, A., Cai, Y., Li, S., & Dubrawski, A. (2024). 
MOMENT: A Family of Open Time-series Foundation Models. arXiv preprint arXiv:2402.03885.

## Glossary [optional]

<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->

[More Information Needed]

## More Information [optional]

[More Information Needed]

## Model Card Authors [optional]

[More Information Needed]

## Model Card Contact