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        "description": "# GluonTS - Probabilistic Time Series Modeling in Python\n\n![PyPI](https://img.shields.io/pypi/v/gluonts.svg?style=flat-square) ![GitHub](https://img.shields.io/github/license/awslabs/gluon-ts.svg?style=flat-square)\n\nGluonTS is a Python toolkit for probabilistic time series modeling,\nbuilt around [Apache MXNet (incubating)](https://mxnet.incubator.apache.org/).\n\nGluonTS provides utilities for loading and iterating over time series datasets,\nstate of the art models ready to be trained, and building blocks to define\nyour own models and quickly experiment with different solutions.\n\n* [Documentation](https://gluon-ts.mxnet.io/)\n* [Paper](https://arxiv.org/abs/1906.05264)\n\n## Installation\n\nGluonTS requires Python 3.6, and the easiest\nway to install it is via `pip`:\n\n```bash\npip install gluonts\n```\n\n## Quick start guide\n\nThis simple example illustrates how to train a model from GluonTS on some data,\nand then use it to make predictions. As a first step, we need to collect\nsome data: in this example we will use the volume of tweets mentioning the\nAMZN ticker symbol.\n\n```python\nimport pandas as pd\nurl = \"https://raw.githubusercontent.com/numenta/NAB/master/data/realTweets/Twitter_volume_AMZN.csv\"\ndf = pd.read_csv(url, header=0, index_col=0)\n```\n\nThe first 100 data points look like follows:\n\n```python\nimport matplotlib.pyplot as plt\ndf[:100].plot(linewidth=2)\nplt.grid(which='both')\nplt.show()\n```\n\n![Data](https://github.com/awslabs/gluon-ts/raw/master/docs/figures/Tweets_AMZN_data.png)\n\nWe can now prepare a training dataset for our model to train on.\nDatasets in GluonTS are essentially iterable collections of\ndictionaries: each dictionary represents a time series\nwith possibly associated features. For this example, we only have one\nentry, specified by the `\"start\"` field which is the timestamp of the\nfirst datapoint, and the `\"target\"` field containing time series data.\nFor training, we will use data up to midnight on April 5th, 2015.\n\n```python\nfrom gluonts.dataset.common import ListDataset\ntraining_data = ListDataset(\n    [{\"start\": df.index[0], \"target\": df.value[:\"2015-04-05 00:00:00\"]}],\n    freq = \"5min\"\n)\n```\n\nA forecasting model in GluonTS is a *predictor* object. One way of obtaining\npredictors is by training a correspondent *estimator*. Instantiating an\nestimator requires specifying the frequency of the time series that it will\nhandle, as well as the number of time steps to predict. In our example\nwe're using 5 minutes data, so `freq=\"5min\"`,\nand we will train a model to predict the next hour, so `prediction_length=12`.\nWe also specify some minimal training options.\n\n```python\nfrom gluonts.model.deepar import DeepAREstimator\nfrom gluonts.trainer import Trainer\n\nestimator = DeepAREstimator(freq=\"5min\", prediction_length=12, trainer=Trainer(epochs=10))\npredictor = estimator.train(training_data=training_data)\n```\n\nDuring training, useful information about the progress will be displayed.\nTo get a full overview of the available options, please refer to the\ndocumentation of `DeepAREstimator` (or other estimators) and `Trainer`.\n\nWe're now ready to make predictions: we will forecast the hour following\nthe midnight on April 15th, 2015.\n\n```python\ntest_data = ListDataset(\n    [{\"start\": df.index[0], \"target\": df.value[:\"2015-04-15 00:00:00\"]}],\n    freq = \"5min\"\n)\n\nfrom gluonts.dataset.util import to_pandas\n\nfor test_entry, forecast in zip(test_data, predictor.predict(test_data)):\n    to_pandas(test_entry)[-60:].plot(linewidth=2)\n    forecast.plot(color='g', prediction_intervals=[50.0, 90.0])\nplt.grid(which='both')\n```\n\n![Forecast](https://github.com/awslabs/gluon-ts/raw/master/docs/figures/Tweets_AMZN_forecast.png)\n\nNote that the forecast is displayed in terms of a probability distribution:\nthe shaded areas represent the 50% and 90% prediction intervals, respectively,\ncentered around the median (dark green line).\n\n## Further examples\n\nThe following are good entry-points to understand how to use\nmany features of GluonTS:\n\n* [Quick Start Tutorial](https://github.com/awslabs/gluon-ts/tree/master/docs/examples/basic_forecasting_tutorial/tutorial.md): a quick start guide.\n* [Extended Forecasting Tutorial](https://github.com/awslabs/gluon-ts/tree/master/docs/examples/extended_forecasting_tutorial/extended_tutorial.md): a detailed tutorial on forecasting.\n* [evaluate_model.py](https://github.com/awslabs/gluon-ts/tree/master/examples/evaluate_model.py): how to train a model and compute evaluation metrics.\n* [benchmark_m4.py](https://github.com/awslabs/gluon-ts/tree/master/examples/benchmark_m4.py): how to evaluate and compare multiple models on multiple datasets.\n\nThe following modules illustrate how custom models can be implemented:\n\n* [`gluonts.model.seasonal_naive`](https://github.com/awslabs/gluon-ts/tree/master/src/gluonts/model/seasonal_naive): how to implement simple models using just NumPy and Pandas.\n* [`gluonts.model.simple_feedforward`](https://github.com/awslabs/gluon-ts/tree/master/src/gluonts/model/simple_feedforward): how to define a trainable, Gluon-based model.\n\n## Contributing\n\nIf you wish to contribute to the project, please refer to our\n[contribution guidelines](https://github.com/awslabs/gluon-ts/tree/master/CONTRIBUTING.md).\n\n## Citing\n\nIf you use GluonTS in a scientific publication, we encourage you to add\nthe following reference to the associated\n[paper](https://arxiv.org/abs/1906.05264):\n\n```\n@article{gluonts,\n  title={{GluonTS: Probabilistic Time Series Modeling in Python}},\n  author={Alexandrov, A. and Benidis, K. and Bohlke-Schneider, M. and\n          Flunkert, V. and Gasthaus, J. and Januschowski, T. and Maddix, D. C.\n          and Rangapuram, S. and Salinas, D. and Schulz, J. and Stella, L. and\n          T\u00fcrkmen, A. C. and Wang, Y.},\n  journal={arXiv preprint arXiv:1906.05264},\n  year={2019}\n}\n```\n\n\n",
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