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# ## ### Welcome to the Univariate-Time-Series-Forecasting wiki!

Univariate time series forecasting using classical methods and deep learning approaches have been performed. In this code the results of time series forecasting for 12 different datasets have been collected. One-step ahead recursive forecasting have been used on each one of them as a univariate time series forecasting problem. The collected data has been plotted based on metrics used. RMSE (root mean squared error) and MSLE (mean logarithmic squared error) have been used as our metrics for comparison.

List of algorithms:

classical methods such as SARIMA or ARIMA
Multilayar Perceptron (MLP)
Stacked Unidirectional and Bidirectional Long Short Term Memory networks (LSTM)
Recurrent Neural Networks (RNN)
Gated Recurrent Unit (GRU)
Convolution based networks (CNN-LSTM)
Dilated Causal Convolutions (DC-CNN)
SeriesNet
WaveNet