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jeffj0110/Interest_Rate_Prediction

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Jeff Jones

April 2021

This is a prototype for Treasury Yield forecasts based on major economic indicators.

This was implemented on the Windows 10 platform with GPU enabled hardware with Python 3.8.8

The following libraries were used -

Package Version


absl-py 0.12.0 argon2-cffi 20.1.0 astunparse 1.6.3 async-generator 1.10 attrs 20.3.0 backcall 0.2.0 bleach 3.3.0 cachetools 4.2.1 certifi 2020.12.5 cffi 1.14.5 chardet 4.0.0 chart-studio 1.1.0 colorama 0.4.4 cycler 0.10.0 decorator 5.0.5 defusedxml 0.7.1 entrypoints 0.3 flatbuffers 1.12 gast 0.3.3 google-auth 1.28.0 google-auth-oauthlib 0.4.4 google-pasta 0.2.0 grpcio 1.32.0 h5py 2.10.0 idna 2.10 importlib-metadata 3.7.3 ipykernel 5.3.4 ipython 7.22.0 ipython-genutils 0.2.0 jedi 0.17.0 Jinja2 2.11.3 joblib 1.0.1 jsonschema 3.2.0 jupyter-client 6.1.12 jupyter-core 4.7.1 jupyterlab-pygments 0.1.2 Keras 2.4.3 Keras-Preprocessing 1.1.2 kiwisolver 1.3.1 Markdown 3.3.4 MarkupSafe 1.1.1 matplotlib 3.4.1 mistune 0.8.4 nbclient 0.5.3 nbconvert 6.0.7 nbformat 5.1.3 nest-asyncio 1.5.1 notebook 6.3.0 numpy 1.19.5 oauthlib 3.1.0 opt-einsum 3.3.0 packaging 20.9 pandas 1.2.4 pandocfilters 1.4.3 parso 0.8.2 pickleshare 0.7.5 Pillow 8.2.0 pip 21.0.1 plotly 4.14.3 prometheus-client 0.10.0 prompt-toolkit 3.0.17 protobuf 3.15.7 pyasn1 0.4.8 pyasn1-modules 0.2.8 pycparser 2.20 Pygments 2.8.1 pyparsing 2.4.7 pyrsistent 0.17.3 python-dateutil 2.8.1 pytz 2021.1 pywin32 227 pywinpty 0.5.7 PyYAML 5.4.1 pyzmq 20.0.0 requests 2.25.1 requests-oauthlib 1.3.0 retrying 1.3.3 rsa 4.7.2 scikit-learn 0.24.1 scipy 1.6.2 Send2Trash 1.5.0 setuptools 52.0.0.post20210125 six 1.15.0 sklearn 0.0 tensorboard 2.4.1 tensorboard-plugin-wit 1.8.0 tensorflow-estimator 2.4.0 tensorflow-gpu 2.4.0 termcolor 1.1.0 terminado 0.9.4 testpath 0.4.4 threadpoolctl 2.1.0 tornado 6.1 traitlets 5.0.5 typing-extensions 3.7.4.3 urllib3 1.26.4 wcwidth 0.2.5 webencodings 0.5.1 Werkzeug 1.0.1 wheel 0.36.2 wincertstore 0.2 wrapt 1.12.1 zipp 3.4.1

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