adding readme

tsfm_public/models/tinytimemixer/README.md

@@ -1,9 +1,58 @@
 
+# TinyTimeMixer (TTM)
+TTM, also known as TinyTimeMixer, are compact pre-trained models for Time-Series Forecasting, open-sourced by 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. The current open-source version supports forecasting use-cases ranging from minutely to hourly resolutions (Ex. 10 min, 15 min, 1 hour, etc.)
+
+**Note that zeroshot, fine-tuning and inference tasks using TTM can easily be executed in 1 GPU machine or in laptops too!!**
+
+**HuggingFace Model Card:** https://huggingface.co/ibm/TTM
+
+
+# Benchmark Highlights:
+- TTM (with less than 1 Million parameters) outperforms the following popular Pre-trained SOTAs demanding several hundred Million to Billions of parameters:
+    - GPT4TS (NeurIPS 23) by 7-12% in few-shot forecasting.
+    - LLMTime (NeurIPS 23) by 24% in zero-shot forecasting.
+    - SimMTM (NeurIPS 23) by 17% in few-shot forecasting.
+    - Time-LLM (ICLR 24) by 8% in few-shot(5%) forecasting
+    - UniTime (WWW 24) by 27% in zero-shot forecasting.
+- Zero-shot results of TTM surpass the few-shot results of many popular SOTA approaches including PatchTST (ICLR 23), PatchTSMixer (KDD 23), TimesNet (ICLR 23), DLinear (AAAI 23) and FEDFormer (ICML 22).
+- TTM (1024-96, released in the HF model card with 1M parameters) outperforms pre-trained MOIRAI-Small (14M parameters) by 10%, MOIRAI-Base (91M parameters) by 2% and MOIRAI-Large (311M parameters) by 3% on zero-shot forecasting (fl = 96) [[notebook]](../../../notebooks/hfdemo/tinytimemixer/ttm_benchmarking_1024_96.ipynb)
+- TTM quick fine-tuning also outperforms the hard statistical baselines (Statistical ensemble and S-Naive) in M4-hourly dataset which existing pretrained TS models are finding hard to outperform. [[notebook]](../../../notebooks/hfdemo/tinytimemixer/ttm_m4_hourly.ipynb)
+- TTM takes only a few seconds for zeroshot/inference and a few minutes for finetuning in 1 GPU machine, as opposed to long timing-requirements and heavy computing infra needs of other existing pretrained models.
+
+# 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 a subset of 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 in few minutes 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, a 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
+- Users have to externally standard scale their data before feeding it to the model (Refer to TSP, our data processing utility for data scaling followed in the below notebooks)
+- Enabling any upsampling or prepending zeros to virtually increase the context length is not recommended and will impact the model performance.
+
+
 # Getting started with TinyTimeMixer (TTM)
 
-## Datasets
+- Getting started notebook: [here](../../../notebooks/hfdemo/ttm_getting_started.ipynb)
+- Illustration notebook for 512-96 model on the considered target datasets: [here](../../../notebooks/hfdemo/tinytimemixer/ttm_benchmarking_512_96.ipynb)
+- Illustration notebook for 1024-96 model on the considered target datasets: [here](../../../notebooks/hfdemo/tinytimemixer/ttm_benchmarking_1024_96.ipynb)
+- M4-hourly transfer learning example: [here](../../../notebooks/hfdemo/tinytimemixer/ttm_m4_hourly.ipynb)
+- Sample pretraining script: [here](../../../notebooks/hfdemo/tinytimemixer/ttm_pretrain_sample.py.ipynb) 
+
+
+
+## Sample Datasets required for the above notebooks
 
-### Examples for multivariate evaluation datasets
 The multivariate evaluation datasets utilized in these scripts are `etth1, etth2, ettm1, ettm2, weather, electricity, traffic`.
 The datasets should be stored in a directory `datasets/` in the follwing structure for the function `get_data` to work.  
 
@@ -13,5 +62,3 @@ The datasets should be stored in a directory `datasets/` in the follwing structu
 - Traffic dataset: "datasets/traffic/traffic.csv"
 
 The datasets can be downloaded from the Informer model's repository https://github.com/zhouhaoyi/Informer2020.
-
-### Example for univariate evaluation dataset