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+# Model Card for TTM
+
+TTM refers to the initial open-source release of Pretrained TinyTimeMixers from IBM Research. With less than 1 Million parameters, TTM 
+introduces the notion of the first-ever “tiny” pre-trained models for Time-Series Forecasting. TTM outperforms several popular benchmarks 
+demanding billions of parameters in zero-shot and few-shot forecasting. TTM is pre-trained on diverse public time-series datasets which can 
+be easily fine-tuned for your target data. Refer to our [paper](https://arxiv.org/pdf/2401.03955.pdf) for more details.
+
+**Note that zeroshot, fine-tuning and inference tasks using TTM can easily be executed in 1 GPU machine or in laptops too!!**
+
+## Model Description
+
+TTM falls under the category of “focused pre-trained models”, wherein each pre-trained TTM is tailored for a particular forecasting 
+setting (governed by the context length and forecast length). Instead of building one massive model supporting all forecasting settings, 
+we opt for the approach of constructing smaller pre-trained models, each focusing on a specific forecasting setting, thereby 
+yielding more accurate results. Furthermore, this approach ensures that our models remain extremely small and exceptionally fast, 
+facilitating easy deployment without demanding a ton of resources. 
+
+Hence, in this model card, we plan to release several pre-trained 
+TTMs that can cater to many common forecasting settings in practice. Additionally, we have released our source code along with 
+our pretraining scripts that users can utilize to pretrain models on their own. Pretraining TTMs is very easy and fast, taking 
+only 3-6 hours using 6 A100 GPUs, as opposed to several days or weeks in traditional approaches.
+
+## Model Releases (along with the branch name where the models are stored):
+
+- 512-96: Given the last 512 time-points (i.e. context length), this model can forecast the next 96 time-points (i.e. forecast length)
+  in future. Recommended for hourly and minutely forecasts (Ex. resolutions 5 min, 10 min, 15 min, etc)  (branch name: main) 
+
+- 1024-96: Given the last 1024 time-points (i.e. context length), this model can forecast the next 96 time-points (i.e. forecast length)
+  in future. Recommended for hourly and minutely forecasts (Ex. resolutions 5 min, 10 min, 15 min, etc) (branch name: 1024-96-v1) 
+
+- Stay tuned for more models !
+
+## Benchmark Highlights:
+
+TTM outperforms pre-trained GPT4TS (NeurIPS 23) by …
+
+TTM outperforms pre-trained LLMTime (NeurIPS 23) by ..
+
+TTM outperforms pre-trained Time-LLM (NeurIPS 23) by ..
+
+TTM outperform pre-trained MOIRAI by …
+
+TTM outperforms other popular benchmarks by ….
+
+TTM also outperforms the hard statistical baselines (Statistical ensemble and S-Naive) in M4-hourly dataset which pretrained TS models are finding hard to outperform.
+
+## Model Details
+
+For more details on TTM architecture and benchmarks, refer to our [paper](https://arxiv.org/pdf/2401.03955.pdf).
+
+TTM-1 currently supports 2 modes:
+
+ - Zeroshot forecasting: Directly apply the pre-trained model on your target data to get an initial forecast (with no training).
+
+ - Finetuned forecasting: Finetune the pre-trained model with your target data to further improve the forecast.
+
+**Since, TTM models are extremely small and fast, it is practically very easy to finetune the model with your available target data to 
+get more accurate forecasts.**
+
+The current release supports multivariate forecasting via both channel independence and channel-mixing approaches. 
+Decoder Channel-Mixing can be enabled during fine-tuning for capturing strong channel-correlation patterns across 
+time-series variates, critical capability lacking in existing counterparts.
+
+In addition, TTM also supports exogenous infusion and categorical data which is not released as part of this version. 
+Stay tuned for these extended features.
+
+## Recommended Use
+1. Users have to standard scale their data before feeding it to the model (Refer to TSP, our data processing utility for data scaling.)
+2. Enabling any upsampling or prepending zeros to virtually increase the context length is not recommended and will
+   impact the model performance.
+
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+
+
+## 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]
+
+## How to Get Started with the Model
+
+[Point notebooks]
+
+## Benchmarks
+
+## Training Data
+
+The TTM models were trained on a collection of datasets from the Monash Time Series Forecasting repository. The datasets used include:
+ - Australian Electricity Demand: https://zenodo.org/records/4659727 
+ - Australian Weather: https://zenodo.org/records/4654822 
+ - Bitcoin dataset: https://zenodo.org/records/5122101 
+ - KDD Cup 2018 dataset: https://zenodo.org/records/4656756 
+ - London Smart Meters: https://zenodo.org/records/4656091 
+ - Saugeen River Flow: https://zenodo.org/records/4656058
+ - Solar Power: https://zenodo.org/records/4656027 
+ - Sunspots: https://zenodo.org/records/4654722
+ - Solar: https://zenodo.org/records/4656144 
+ - US Births: https://zenodo.org/records/4656049 
+ - Wind Farms Production data: https://zenodo.org/records/4654858 
+ - Wind Power: https://zenodo.org/records/4656032
+
+
+## Citation [optional]
+Kindly cite the following paper, if you intend to use our model or its associated architectures/approaches in your 
+work
+
+**BibTeX:**
+
+@article{ekambaram2024ttms,
+  title={TTMs: Fast Multi-level Tiny Time Mixers for Improved Zero-shot and Few-shot Forecasting of Multivariate Time Series},
+  author={Ekambaram, Vijay and Jati, Arindam and Nguyen, Nam H and Dayama, Pankaj and Reddy, Chandra and Gifford, Wesley M and Kalagnanam, Jayant},
+  journal={arXiv preprint arXiv:2401.03955},
+  year={2024}
+}
+
+**APA:**
+
+Ekambaram, V., Jati, A., Nguyen, N. H., Dayama, P., Reddy, C., Gifford, W. M., & Kalagnanam, J. (2024). TTMs: Fast Multi-level Tiny Time Mixers for Improved Zero-shot and Few-shot Forecasting of Multivariate Time Series. arXiv preprint arXiv:2401.03955.
+
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]
+
+## IBM Public Repository Disclosure: 
+
+All content in this repository including code has been provided by IBM under the associated 
+open source software license and IBM is under no obligation to provide enhancements, 
+updates, or support. IBM developers produced this code as an 
+open source project (not as an IBM product), and IBM makes no assertions as to 
+the level of quality nor security, and will not be maintaining this code going forward.