add getting started to README and add license

LICENSE

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+BSD 3-Clause License
+
+Copyright (c) 2023, SAKURA internet Inc.
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are met:
+
+1. Redistributions of source code must retain the above copyright notice, this
+   list of conditions and the following disclaimer.
+
+2. Redistributions in binary form must reproduce the above copyright notice,
+   this list of conditions and the following disclaimer in the documentation
+   and/or other materials provided with the distribution.
+
+3. Neither the name of the copyright holder nor the names of its
+   contributors may be used to endorse or promote products derived from
+   this software without specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
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+OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

README.md

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 # MetricSifter
+![CI workflow](https://github.com/ai4sre/metricsifter/actions/workflows/ci.yaml/badge.svg)
 
-MetricSifter is the implementation of feature reduction of multivariate time series data for fault localization in Python.
+## Introduction
+
+TBD
+
+## Installation
+
+You can install `metricsifter` package from PyPI via `pip install metricsifter`.
+
+## Getting Started
+
+```python
+from metricsifter.sifter import Sifter
+from tests.sample_gen.generator import generate_synthetic_data
+
+## Prepare time series data
+normal_data, abonormal_data, _, _, anomalous_nodes = generate_synthetic_data(num_node=20, num_edge=20, num_normal_samples=55, num_abnormal_samples=15, anomaly_type=0)
+data = pd.concat([normal_data, abonormal_data], axis=0, ignore_index=True)
+
+sifter = Sifter(penalty_adjust=2.0, n_jobs=1)
+sifted_data = sifter.run(data=data)
+print("(#removed metrics) / (#total metrics):", len(set(data.columns) - set(siftered_data.columns)), "/", len(data.columns))
+print("difference between prediction and ground truth:", set(siftered_data.columns) - anomalous_nodes)
+assert set(sifted_data.columns) - anomalous_nodes == set()
+```
+
+The example of original synthetic data and its sifted data is shown in the following figure.
+
+![original synthetic data](./docs/images/original_time_series.png)
+![sifted synthetic data](./docs/images/sifted_time_series.png)
+
+
+## For developers
+
+Run test cases with the following commands.
+
+```shell
+# Install dependencies for development
+python -m pip install -r requirements-dev.txt
+# Run test cases
+pytest -s -v tests
+```
+
+## License
+
+[BSD-3-Clause](LICENSE)