Online detection of continuous changes in stochastic processes

Miyaguchi, Kohei; Yamanishi, Kenji

doi:10.1007/s41060-017-0045-2

Cited by 11 publications

(13 citation statements)

References 22 publications

(29 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…On the other hand, the same function means to assume an imaginary earthquake at a random time in Condition B. Details of the reason for choosing the evaluation method above mentioned in Appendix E referring to the literature [ 20 , 75 , 76 , 77 , 78 ]. In summary, the alarming functions are compared as in the following procedure.…”

Section: Discussionmentioning

confidence: 99%

“…For example, the eigenvectors and the corresponding eigenvalues of the N ∗ N matrix representing the pairwise co-occurrences of earthquakes in N regions have been used to predict the probability of earthquake occurrences in clusters of regions [ 17 ]. Machine learning techniques used to detect the times of high change point score [ 18 , 19 , 20 ], based on the transition of models on latent dynamics before and after time t , may also have the potential to discover an essential change in land crust behavior. However, the precursors of large earthquakes have been difficult to capture using this approach because of their complex and unknown latent dynamics and extremely low frequency of occurrence.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Regional Seismic Information Entropy to Detect Earthquake Activation Precursors

Ohsawa

2018

Entropy

View full text Add to dashboard Cite

A method is presented to detect earthquake precursors from time series data on earthquakes in a target region. The Regional Entropy of Seismic Information (RESI) is an index that represents the average influence of an earthquake in a target region on the diversity of clusters to which earthquake foci are distributed. Based on a simple qualitative model of the dynamics of land crust, it is hypothesized that the saturation that occurs after an increase in RESI precedes the activation of earthquakes. This hypothesis is validated by the earthquake catalog. This temporal change was found to correlate with the activation of earthquakes in Japanese regions one to two years ahead of the real activation, more reliably than the compared baseline methods.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Regional Seismic Information Entropy to Detect Earthquake Activation Precursors

Ohsawa

2018

Entropy

View full text Add to dashboard Cite

show abstract

“…Hill et al [6] applied an autoregressive model to detecting anomalies according to deviation from normal data, and it is broadly assumed that anomaly scores rise sharply with general anomaly detection. Miyaguchi et al [14] proposed a method that can be applied when scores rise gradually. Online anomaly detection is extremely fast, but the model for online anomaly detection is relatively simple and does not deal well with irregular and complicated waveforms.…”

Section: Related Workmentioning

confidence: 99%

dLSTM: a new approach for anomaly detection using deep learning with delayed prediction

Maya

Ueno

Nishikawa

2019

Int J Data Sci Anal

View full text Add to dashboard Cite

In this paper, we propose delayed Long Short-Term Memory (dLSTM), an anomaly detection method for time-series data. We first build a predictive model from normal (non-anomalous) training data, then perform anomaly detection based on the prediction error for observed data. However, there are multiple states in the waveforms of normal data, which may lower prediction accuracy. To deal with this problem, we utilize multiple prediction models based on LSTM for anomaly detection. In this scheme, the prediction accuracy strongly depends on the method of selecting a proper predictive model from multiple possible models. We propose a novel method to determine the proper predictive model for anomaly detection. Our approach provides multiple predicted value candidates in advance and selects the one that is closest to the measured value. We delay the model selection until the corresponding measured values are acquired. Using this concept for anomaly detection, dLSTM selects the proper predictive model to enhance prediction accuracy. In our experimental evaluation using real and artificial data, dLSTM detects anomalies more accurately than methods in comparison.

show abstract

“…The direct reflection of changes in the graph structure is a feature of GBE that differentiates it from the previous methods for change detection. Among them, Local Linear Regression (LLR [7]) is taken as a baseline in the experimental comparison in Sects. 4 and 5.…”

Section: Graph-based Entropy As An Index Of Explanatory Diversitymentioning

confidence: 99%

Graph-Based Entropy for Detecting Explanatory Signs of Changes in Market

Ohsawa

2018

Rev Socionetwork Strat

View full text Add to dashboard Cite

Graph-based entropy, an index of the diversity of events in their distribution to parts of a co-occurrence graph, is proposed for detecting signs of structural changes in the data that are informative in explaining latent dynamics of consumers' behavior. For obtaining graph-based entropy, connected sub-graphs are first obtained from the graph of co-occurrences of items in the data. Then, the distribution of items occurring in events in the data to these sub-graphs is reflected on the value of graph-based entropy. For the data on the position of sale, a change in this value is regarded as a sign of the appearance, the separation, the disappearance, or the uniting of consumers' interests. These phenomena are regarded as the signs of dynamic changes in consumers' behavior that may be the effects of external events and information. Experiments show that graph-based entropy outperforms baseline methods that can be used for change detection, in explaining substantial changes and their signs in consumers' preference of items in supermarket stores.

show abstract

Online detection of continuous changes in stochastic processes

Cited by 11 publications

References 22 publications

Regional Seismic Information Entropy to Detect Earthquake Activation Precursors

Regional Seismic Information Entropy to Detect Earthquake Activation Precursors

dLSTM: a new approach for anomaly detection using deep learning with delayed prediction

Graph-Based Entropy for Detecting Explanatory Signs of Changes in Market

Contact Info

Product

Resources

About