  Time series forecasting has gained lots of attention recently; this is
because many real-world phenomena can be modeled as time series. The massive
volume of data and recent advancements in the processing power of the computers
enable researchers to develop more sophisticated machine learning algorithms
such as neural networks to forecast the time series data. In this paper, we
propose various neural network architectures to forecast the time series data
using the dynamic measurements; moreover, we introduce various architectures on
how to combine static and dynamic measurements for forecasting. We also
investigate the importance of performing techniques such as anomaly detection
and clustering on forecasting accuracy. Our results indicate that clustering
can improve the overall prediction time as well as improve the forecasting
performance of the neural network. Furthermore, we show that feature-based
clustering can outperform the distance-based clustering in terms of speed and
efficiency. Finally, our results indicate that adding more predictors to
forecast the target variable will not necessarily improve the forecasting
accuracy.
