Much of the hype surrounding neural networks is about image-based applications. However, Recurrent Neural Networks (RNNs) have been successfully used in recent years to predict future events in time series as well. RNNs have contributed to breakthroughs in a wide variety of fields centered around predicting sequences of events. In this piece, however, we'll demonstrate how one type of RNN, the Long Short-Term Memory (LSTM) network, can be used to predict even financial time series data—perhaps the most chaotic and difficult of all time series.
The use of Twitter and natural language processing opens up a promising new approach to flu surveillance. Such data-driven methods produce encouraging results and provide a faster way to identify flu surges.
Further, these Twitter-based methods can be very easily applied to numerous other domains such as Marketing, for identifying geospatial trends in brand image, as well as in Urban Planning for analyzing public attitudes towards various spaces and landmarks for example.
Machine Learning is still in its infancy in archaeology, but the approach holds enormous promise. Just as in finance and disaster mitigation, the use of satellite imagery has become a vital resource for archaeology. As more archaeologists around the world automate the process of identifying archaeological sites with satellite images, sites will be found at a faster rate than ever before.
Recently, there has been great success in time series analyses by applying dynamic time warping (DTW). Indeed, when combined with simple algorithms such as k-Nearest Neighbours, DTW has reproduced accuracies of state-of-the-art algorithms, such as deep neural nets etc.
We will discuss here a supervised classification example of wearable devices and a possible use-case for DTW.