5. Conclusion

Time series pose important challenges to existing approaches which perform predictive modeling for classification tasks. In this paper we present a review on our previous works. Our contributions are aggregated into two categories: data-level and algorithm-level approaches. Our data-level approach consists of encoding time series using STin order to produce noise-free input signals which offer a more efficient CNN training. At the algorithm level, one approach is the adaptive convolutional layer filter approach which consists of determining the size of the filter based on an analysis of the input time series signals and fluctuations present within them. Indeed, choosing the proper 1st layer filter generates features maps which are more informative about the input signals and which capture the whole peaks within input signals. Furthermore, "TL SVM similar domains" and "TL SVM across domains" are algorithm-level approaches dealing with tasks with limited annotated data, which are regarded as two global, fast and light-weight techniques for these kinds of tasks. These two CNN approaches generate features general and global enough to recognize time series of the target learning task, given time series of a source learning task that is similar or different but related to the target learning task. All these approaches were implemented on the recognition of human activities, including normal activities performed by typical subjects and disorder-based activities performed by atypical subjects (such as SMMs of autistic subjects). Experimental results have showed the superiority of our techniques and their ability to extract relevant features from time series inputs. As a perspective, knowing that time series datasets often contain outliers either due to noisy time series or mislabeled time series (e.g. incorrect labels), we aim at studying a robust CNN that is insensitive to outliers. As opposed to our data-level CNN technique (mentioned in this paper) whose goal is to eliminate noise from time series, this robust CNN is an algorithm-level technique with acts at the level of loss functions by controlling high error values caused by outliers.
