Proposed Model: Ensemble Learning

As our first step, we plan to remove outliers/noise from our dataset which diverges our forecasting from the actual trend. We observed that the algorithms performed better on a particular type of time-series data. Like for time series with more seasonal component prophet approach would give better results. Classical methods like ETS and ARIMA give better results with short term dependencies in time series whereas complex models like RNN/LSTM gave better results when there was long term correlation in time series.

 Thus we plan to do Ensemble learning for web traffic prediction. Ensemble learning combines multiple predictions (forecasts) from one or multiple methods to overcome the accuracy of simple prediction and to avoid possible overfit. The models that we would be working with are as follows –

1)     Ensembles of classical models-

·        Autoregressive (AR),

·        Moving Average (MA),

·        Autoregressive Moving Average (ARMA),

·        Autoregressive Integrated Moving Average (ARIMA), and

·        Seasonal Autoregressive Integrated Moving Average (SARIMA) models.

2)     Ensembles of LSTM models

3)     Ensembles of Prophet

Learn More Abot ensemble Learning: https://towardsdatascience.com/ensemble-methods-in-machine-learning-what-are-they-and-why-use-them-68ec3f9fef5f