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+## Authors
+* [**Md Mijanur Rahman**](https://github.com/mijanr)
+# This repository contains the code for time-series (TS) classification with various state-of-the-art TS classification models. 
+
+Entire pipeline is developed in a way such that an easy integration of ***mlflow***, ***hydra*** and ***optuna sweeper*** is possible.
+
+1. The simplest way to run a model on a specific dataset is to run the following command in the terminal, in the root directory of the repository:
+```bash
+python main.py
+```
+This will run a model on a dataset specified in the config file `main_config.yaml`, located in the `config` directory. 
+
+2. To optimize hyperparameters of a model, run the following command in the terminal, in the root directory of the repository:
+```bash
+python main.py --multirun
+```
+This will run a model on a dataset specified in the config file `main_config.yaml`, located in the `config` directory. However, this time, a search space, specified in `config/search_space/model_name` will be used by optuna to find the optimal hyperparameters. A total number of trial is specified in the `main_config.yaml` file.
+
+3. To run a model on a specific dataset, run the following command in the terminal, in the root directory of the repository:
+```bash
+python main.py "dataset_name=[Handwriting]" 
+```
+For a multirun case:
+```bash
+python main.py --multirun "dataset_name=[Handwriting]"  
+```
+To run for a specific model:
+```bash
+python main.py --multirun "dataset_name=[Handwriting]" "models=LSTM_FCN"
+```
+Model name can be anything that is available in the `codes/models` directory, given corresponding configs are also available.
+
+Similarly, other parameters can also be specified in the terminal, and passed as arguments. 
+## Mlruns
+All the runs are stored in the `mlruns` directory. To visualize the runs, run the following command in the terminal, in the root directory of the repository:
+```bash
+mlflow ui
+```
+This will start a server, and the runs can be visualized in the browser at `localhost:5000`.
+
+## Requirements
+requirements.yml file contains all the dependencies required to run the code. To install all the dependencies, run the following command in the terminal, given that anaconda is installed:
+```bash
+conda env create -f requirements.yaml
+```
+This will create a conda environment named `ts_cl` with all the dependencies installed.
+It insall Pytorch with CPU support. To install Pytorch with GPU support, follow the instructions given [here](https://pytorch.org/get-started/locally/).
+
+## Datasets
+This repository uses the datasets from the [UEA & UCR Time Series Classification Repository](https://www.timeseriesclassification.com/). The datasets are automatically downloaded and stored in the `data` directory.
+
+## Models
+We use the classification models available in [tsai library](https://timeseriesai.github.io/tsai/). Models can be added to this repository by adding the corresponding config file in the `config` directory, and the corresponding model file in the `codes/models` directory.
+
+## Results
+You can find the results in the following table:
+
+<!--START-->
+
+<!--END-->
+
+
 ## Authors
 * [**Md Mijanur Rahman**](https://github.com/mijanr)