https://github.com/srineogi/time-series-modelling-deep-learning-python
TIME SERIES Forecasting with LSTM, GRU & Temporal CNN
https://github.com/srineogi/time-series-modelling-deep-learning-python
convolutional-neural-networks gru keras lstm-model lstm-neural-networks machine-learning tcnn tensorflow time-series-forecasting
Last synced: 8 months ago
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TIME SERIES Forecasting with LSTM, GRU & Temporal CNN
- Host: GitHub
- URL: https://github.com/srineogi/time-series-modelling-deep-learning-python
- Owner: SriNeogi
- Created: 2024-12-12T16:05:33.000Z (11 months ago)
- Default Branch: main
- Last Pushed: 2024-12-24T07:32:21.000Z (10 months ago)
- Last Synced: 2025-01-06T10:18:06.258Z (10 months ago)
- Topics: convolutional-neural-networks, gru, keras, lstm-model, lstm-neural-networks, machine-learning, tcnn, tensorflow, time-series-forecasting
- Language: Jupyter Notebook
- Homepage:
- Size: 233 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# About
LSTM is a special type of RNN. LSTMs are used for sequential data. Here we are using it for univariate time series modelling.
## Dataset
This is horizontal time series data set. Each row contains unique Sales Product-Market combinations. There are 19 such rows. Each time series has 36 time steps.