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time-series's Introduction

Time-Series

time-series is a Python module for machine learning for time-series built on top of tensorflow and is distributed under the MIT license.

This repository was created as a companion repository for chapter 12, Multivariate Forecasting, of the book Machine Learning for Time-Series with Python.

Tensorflow implementations of Time-Series models including these:

  • Amazon DeepAR
  • Gaussian Processes
  • LSTM
  • TCN
  • Transformer, and
  • NBEATS

The time_series.dataset package, part of this library, includes utility functions for loading datasets.

Please see the example notebook for usage and training results.

Installation

pip install git+https://github.com/benman1/time-series.git

Contribute

Pull requests welcome!

List of Contributors

Contributions from various people have found their way into this repository. Thanks to everyone for their hard work!

time-series's People

Contributors

arrigonialberto86 avatar benman1 avatar ketan-b avatar

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time-series's Issues

data processing

hello author,

I have some doubts. I am new to transformers. I have original data with shape (1000, 50) and convert to time series data as follows
(total_samples=1000, window_or_timestep=10, feature_dim=50).==================> (1000,10,50).
So I have 2 cases. First is original data reshape add dimension (1000, 50, 1). Second is time series converted data (1000, 10, 50)
For RNN/LSTM I use second case.
What should be the input data shape should i use for transformers? is it first (50,1) or second (10, 50)
please give me your feedback.

thank you

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