On Extracting Probability Distribution Information from Time Series
نویسندگان
چکیده
منابع مشابه
On Extracting Probability Distribution Information from Time Series
Time-series (TS) are employed in a variety of academic disciplines. In this paper we focus on extracting probability density functions (PDFs) from TS to gain an insight into the underlying dynamic processes. On discussing this “extraction” problem, we consider two popular approaches that we identify as histograms and Bandt–Pompe. We use an information-theoretic method to objectively compare the...
متن کاملExtracting Time-evolving Latent Skills from Examination Time Series
Examination results are used to judge whether an examinee possesses the desired latent skills. In order to grasp the skills, it is important to find which skills a question item contains. The relationship between items and skills may be represented by what we call a Q-matrix. Recent studies have been attempting to extract a Q-matrix with non-negative matrix factorization (NMF) from a set of exa...
متن کاملON THE STATIONARY PROBABILITY DENSITY FUNCTION OF BILINEAR TIME SERIES MODELS: A NUMERICAL APPROACH
In this paper, we show that the Chapman-Kolmogorov formula could be used as a recursive formula for computing the m-step-ahead conditional density of a Markov bilinear model. The stationary marginal probability density function of the model may be approximated by the m-step-ahead conditional density for sufficiently large m.
متن کاملExtracting Driving Signals from Non-Stationary Time Series
We propose a simple method for the reconstruction of slow dynamics perturbations from non-stationary time series records. The method traces the evolution of the perturbing signal by simultaneously learning the intrinsic stationary dynamics and the time dependency of the changing parameter. For this purpose, an extra input unit is added to a feedforward artificial neural network and a suitable e...
متن کاملExtracting Motifs from Time Series Generated by Concurrent Activities
In this article, we present a model for un-supervised extraction of motifs from multivariate time series. We consider a particular kind of time series where observed values are caused by the superposition of multiple phenomena occuring concurrently and with no synchronization.
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: Entropy
سال: 2012
ISSN: 1099-4300
DOI: 10.3390/e14101829