MultiFlow: Leveraging Combined Time Series Algorithms for Accurate Traffic Forecasting

Anticipating website traffic is a significant issue today because it might interfere with the functioning of important websites. Research on time-series forecasting has been very active. Predicting future time series values is one of the most difficult problems in the field. This study of time series data covers a wide range of topics, from classification and forecasting to inference and analysis. The forecast is typically accurate when the time series databases are utilized with the quickest growth rates, despite the fact that it can be predicted using a variety of techniques. The work proposed is a unique deep learning-based time series hybrid model dependent on regression, which is a combination of different algorithms for predicting web traffic.