Mining lake time series using symbolic representation
نویسندگان
چکیده
منابع مشابه
Segmented shape-symbolic time series representation
This paper introduces a symbolic time series representation using monotonic sub-sequences and bottom up segmentation. The representation minimizes the square error between the segments and their monotonic approximations. The representation can robustly classify the direction of a segment and is scale invariant with respect to the time and value dimensions. This paper describes two experiments. ...
متن کاملTime-Series Segmentation and Symbolic Representation, from Process-Monitoring to Data-Mining
Data-analysis has undergone an important change from statistical descriptive analysis to data-mining. Information networks and huge data-storage equipments brought data-retrieval to new dimensions. Time-series are especially easy to accumulate as digital sensors can be used to fill databases without any intervention. This is both a boon and a problem as the very amount of data available prevent...
متن کامل1d-SAX: A Novel Symbolic Representation for Time Series
SAX (Symbolic Aggregate approXimation) is one of the main symbolization technique for time series. A well-known limitation of SAX is that trends are not taken into account in the symbolization. This paper proposes 1d-SAX a method to represent a time series as a sequence of symbols that contain each an information about the average and the trend of the series on a segment. We compare the efficie...
متن کاملSymbolic Representation of Time Series: A Hierarchical Coclustering Formalization
The choice of an appropriate representation remains crucial for mining time series, particularly to reach a good trade-o between the dimensionality reduction and the stored information. Symbolic representations constitute a simple way of reducing the dimensionality by turning time series into sequences of symbols. SAXO is a data-driven symbolic representation of time series which encodes typica...
متن کاملDeep Symbolic Representation Learning for Heterogeneous Time-series Classification
In this paper, we consider the problem of event classification with multi-variate time series data consisting of heterogeneous (continuous and categorical) variables. The complex temporal dependencies between the variables combined with sparsity of the data makes the event classification problem particularly challenging. Most state-of-art approaches address this either by designing hand-enginee...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Ecological Informatics
سال: 2017
ISSN: 1574-9541
DOI: 10.1016/j.ecoinf.2017.03.001