Forecasting customer traffic of a specific form of transportation using SEVEN different forecasting methods based on past traffic data and performing comparative analysis in terms of RMSE.
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Updated
Jun 10, 2019 - Python
Forecasting customer traffic of a specific form of transportation using SEVEN different forecasting methods based on past traffic data and performing comparative analysis in terms of RMSE.
GETS - Time Series Forecasting Framework using Grammatical Evolution.
Recurrent Graph Evolution Neural Network (ReGENN) using Graph Soft Evolution (GSE)
An experiemtal review on deep learning architectures for time series forecasting
Keras, Tensorflow eager execution layers for exponential smoothing
Time series prediction using dilated causal convolutional neural nets (temporal CNN)
Keras/Pytorch implementation of N-BEATS: Neural basis expansion analysis for interpretable time series forecasting.
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