The Tsaoling Landslide in Taiwan has captured attentions of researchers worldwide due to its frequent catastrophic failure and distinctive features. Thanks to the launch of TerraSAR-X/TanDEM-X (TSX/TDX) constellation, retrieval of global DEM with high spatial resolution and accuracy becomes possible, which is extremely useful for the study of natural hazards (e.g., landslides) globally. We atte...

The sporadic behavior of wind currently hampers electrical companies from fully utilizing wind energy as a viable resource. Observations of reported wind generation data suggest that there may be diurnal patterns in wind generation. Predicting the magnitude and graphical shape of wind generation at different times during the day would help control room operators to better direct the assignment ...

We propose a method for filling gaps and removing interferences in time series for applications involving continuous monitoring of environmental variables. The approach is non-parametric and based on an iterative pattern-matching between the affected and the valid parts of the time series. It considers several variables jointly in the pattern matching process and allows preserving linear or non...

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...

A financial decision supporting system based on the fuzzy candlestick pattern is proposed and developed. We model Japanese candlestick patterns by using fuzzy linguistic variables. Japanese candlestick theory is an empirical model of investment decision. The theory assumes that the trend of financial time series could be predicted by identifying specific candlestick patterns in the candlestick ...