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‎.gitignore

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/.idea/*
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/__pycache__/*
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.gitignore
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/.ipynb_checkpoints/*
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/notebook/.ipynb_checkpoints
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/backup/*
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.DS_Store
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MYIDEA.md
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/deepts/.DS_Store
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/deepts/models/.DS_Store
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/examples/.DS_Store
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/temp.py
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/models/scaler.pkl
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/models/saved_model.pb
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/models/variables/*
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**/__pycache__/
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**/.DS_Store
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**/.ipynb_checkpoints/
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/examples/data/crawer_data.py
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/reference/*
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/models/*

‎LICENSE

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MIT License
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Copyright (c) 2020 Longxing Tan
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Permission is hereby granted, free of charge, to any person obtaining a copy
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of this software and associated documentation files (the "Software"), to deal
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in the Software without restriction, including without limitation the rights
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to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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copies of the Software, and to permit persons to whom the Software is
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furnished to do so, subject to the following conditions:
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The above copyright notice and this permission notice shall be included in all
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copies or substantial portions of the Software.
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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SOFTWARE.

‎README.md

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# Time series prediction
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This repo implements the common methods of time series prediction, especially deep learning methods in TensorFlow2.
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It's highly welcomed to contribute if you have better idea, just create a PR. If any question, feel free to open an issue.
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<table style="width:100%">
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<tr>
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<th>
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<p align="center">
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ARIMA
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</p>
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</th>
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<th>
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<p align="center">
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<a href="./docs/arima.md" name="introduction">intro</a>
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</p>
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<p align="center">
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<a href="./deepts/models/arima.py" name="code">code</a>
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</p>
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</th>
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</tr>
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<tr>
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<th>
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<p align="center">
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GBDT
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</p>
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</th>
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<th>
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<p align="center">
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<a href="./docs/tree.md" name="introduction">intro</a>
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</p>
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<p align="center">
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<a href="./deepts/models/tree.py" name="code">code</a>
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</p>
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</th>
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</tr>
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<tr>
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<th>
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<p align="center">
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RNN
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</p>
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</th>
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<th>
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<p align="center">
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<a href="./docs/rnn.md" name="introduction">intro</a>
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</p>
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<p align="center">
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<a href="./deepts/models/seq2seq.py" name="code">code</a>
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</p>
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</th>
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</tr>
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<tr>
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<th>
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<p align="center">
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wavenet
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</p>
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</th>
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<th>
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<p align="center">
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<a href="./docs/wavenet.md" name="introduction">intro</a>
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</p>
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<p align="center">
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<a href="./deepts/models/wavenet.py" name="code">code</a>
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</p>
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</th>
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</tr>
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<tr>
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<th>
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<p align="center">
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transformer
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</p>
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</th>
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<th>
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<p align="center">
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<a href="./docs/transformer.md" name="introduction">intro</a>
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</p>
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<p align="center">
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<a href="./deepts/models/transformer.py" name="code">code</a>
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</p>
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</th>
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</tr>
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<tr>
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<th>
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<p align="center">
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U-Net
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</p>
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</th>
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<th>
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<p align="center">
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<a href="./docs/unet.md" name="introduction">intro</a>
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</p>
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<p align="center">
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<a href="./deepts/models/unet.py" name="code">code</a>
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</p>
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</th>
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</tr>
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<tr>
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<th>
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<p align="center">
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n-beats
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</p>
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</th>
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<th>
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<p align="center">
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<a href="./docs/nbeats.md" name="introduction">intro</a>
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</p>
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<p align="center">
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<a href="./deepts/models/nbeats.py" name="code">code</a>
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</p>
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</th>
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</tr>
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<tr>
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<th>
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<p align="center">
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GAN
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</p>
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</th>
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<th>
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<p align="center">
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<a href="./docs/gan.md" name="introduction">intro</a>
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</p>
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<p align="center">
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<a href="./deepts/models/gan.py" name="code">code</a>
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</p>
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</th>
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</tr>
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</table>
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## Usage
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1. Install the library
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```bash
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pip install -r requirements.txt
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```
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2. Download the data, if necessary
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```bash
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bash ./data/download_passenger.sh
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```
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3. Train the model, set `custom_model_params` if you want
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```bash
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cd examples
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python run_train.py --use_model seq2seq
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```
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4. Predict new data
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```bash
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python run_test.py
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```
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## Further reading
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https://github.com/awslabs/gluon-ts/
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## Contributor
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- [LongxingTan](https://longxingtan.github.io/)

‎data/.gitkeep

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‎data/download_passenger.sh

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#!/bin/sh
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# This is a simple script to download air passenger data
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wget https://www.kaggle.com/andreazzini/international-airline-passengers/download -O data/international-airline-passengers.zip

‎data/international-airline-passengers.csv

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"Month","International airline passengers: monthly totals in thousands. Jan 49 ? Dec 60"
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"1949-01",112
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"1949-02",118
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"1949-03",132
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"1949-04",129
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"1949-05",121
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"1949-06",135
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"1949-07",148
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"1949-08",148
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"1949-09",136
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"1949-10",119
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"1949-11",104
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"1949-12",118
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"1950-01",115
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"1950-02",126
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"1950-03",141
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"1950-04",135
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"1950-05",125
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"1950-06",149
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"1950-07",170
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"1950-08",170
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"1950-09",158
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"1950-10",133
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"1950-11",114
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"1950-12",140
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"1951-01",145
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"1951-02",150
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"1951-03",178
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"1951-04",163
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"1951-05",172
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"1951-06",178
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"1951-07",199
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"1951-08",199
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"1951-09",184
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"1951-10",162
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"1951-11",146
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"1951-12",166
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"1952-01",171
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"1952-02",180
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"1952-03",193
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"1952-04",181
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"1952-05",183
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"1952-06",218
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"1952-07",230
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"1952-08",242
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"1952-09",209
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"1952-10",191
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"1952-11",172
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"1952-12",194
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"1953-01",196
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"1953-02",196
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"1953-03",236
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"1953-04",235
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"1953-05",229
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"1953-06",243
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"1953-07",264
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"1953-08",272
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"1953-09",237
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"1953-10",211
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"1953-11",180
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"1953-12",201
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"1954-01",204
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"1954-02",188
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"1954-03",235
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"1954-04",227
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"1954-05",234
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"1954-06",264
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"1954-07",302
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"1954-08",293
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"1954-09",259
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"1954-10",229
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"1954-11",203
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"1954-12",229
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"1955-01",242
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"1955-02",233
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"1955-03",267
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"1955-04",269
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"1955-05",270
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"1955-06",315
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"1955-07",364
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"1955-08",347
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"1955-09",312
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"1955-10",274
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"1955-11",237
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"1955-12",278
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"1956-01",284
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"1956-02",277
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"1956-03",317
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"1956-04",313
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"1956-05",318
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"1956-06",374
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"1956-07",413
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"1956-08",405
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"1956-09",355
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"1956-10",306
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"1956-11",271
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"1956-12",306
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"1957-01",315
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"1957-02",301
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"1957-03",356
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"1957-04",348
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"1957-05",355
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"1957-06",422
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"1957-07",465
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"1957-08",467
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"1957-09",404
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"1957-10",347
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"1957-11",305
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"1957-12",336
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"1958-01",340
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"1958-02",318
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"1958-03",362
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"1958-04",348
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"1958-05",363
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"1958-06",435
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"1958-07",491
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"1958-08",505
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"1958-09",404
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"1958-10",359
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"1958-11",310
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"1958-12",337
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"1959-01",360
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"1959-02",342
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"1959-03",406
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"1959-04",396
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"1959-05",420
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"1959-06",472
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"1959-07",548
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"1959-08",559
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"1959-09",463
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"1959-10",407
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"1959-11",362
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"1959-12",405
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"1960-01",417
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"1960-02",391
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"1960-03",419
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"1960-04",461
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"1960-05",472
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"1960-06",535
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"1960-07",622
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"1960-08",606
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"1960-09",508
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"1960-10",461
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"1960-11",390
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"1960-12",432
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International airline passengers: monthly totals in thousands. Jan 49 ? Dec 60
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‎deepts/__init__.py

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# encoding=utf-8

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