Recent researches have seen an upsurge in the analysis of consumer reviews. Although, several dimensions have been explored, less is known on the temporal dynamics of events that happen over the lifecycle of online products. What are the dominant sales patterns? How are they affected by review count, rating, helpfulness and sentiment? How is trust characterized and what are its effects on sales...

Rotation moment invariants have been of great interest in image processing and pattern recognition. This paper presents a novel kind of rotation moment invariants based on the Slepian functions, which were originally introduced in the method of separation of variables for Helmholtz equations. They were first proposed for time series by Slepian and his coworkers in the 1960s. Recent studies have...

Given a set of time series, we aim at finding representatives which best comprehend the recurring temporal patterns contained in the data. We demonstrate BestTime, a Matlab application that uses recurrence quantification analysis to find time series representatives.

Change detection in satellite image time series is an important domain with various applications in land study. Most previous works proposed to perform this detection by studying two images and analysing their differences. However, those methods do not exploit the whole set of images that is available today and they do not propose a description of the detected changes. We propose a sequential p...

A new methodology for automated extraction of repeated patterns in time-series data is presented, aimed in particular at the analysis of musical sequences. The basic principles consists in a search for closed patterns in a multi-dimensional parametric space. It is shown that this basic mechanism needs to be articulated with a periodic pattern discovery system, implying therefore a strict chrono...

Segmentation analysis addresses the following data analysis problem: given a time series, find a partitioning of the sequence to segments that are internally homogenous with respect to the desired pattern language and cost function. We will consider applications of segmentation analysis towards analysis of paleoecological spatio-temporal time series data. Our emphasis is both on computational a...

An increasing number of computer vision and pattern recognition problems require structured regression techniques. Problems like human pose estimation, unsegmented action recognition, emotion prediction and facial landmark detection have temporal or spatial output dependencies that regular regression techniques do not capture. In this paper we present continuous conditional neural fields (CCNF)...

This paper introduces a framework that mines temporal patterns in complex multivariate time series data. Since multivariate temporal time series could be noisy and inaccurately reported, first they are converted into time-interval sequences. Such sequences are then used to build temporal patterns backwards in time using temporal operators, which describe relation between two sequences (before a...

Top-k Case Matching (TKCM). To impute a missing value in a time series s at time tn: 1. Define query pattern P (tn), spanning the values of d reference time series over a time frame of l time points anchored at time tn 2. Look for the k most similar non-overlapping patterns in a sliding window over the time series 3. Impute the missing value s(tn) as the average of the values of s at the anchor...

Partial periodic patterns are commonly seen in real-world applications. The major problem of mining partial periodic patterns is the efficiency problem due to a huge set of partial periodic candidates. Although some efficient algorithms have been developed to tackle the problem, the performance of the algorithms significantly drops when the mining parameters are set low. In the past, the author...