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Clustering Financial Time Series by Dependency
In this paper, we propose a procedure for clustering financial time series by dependency on their volatilities. Our procedure is based on the...
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Multiresolution Data Analytics for Financial Time Series Using MATLAB
In this chapter, we explore the use of multiresolution analysis techniques, including wavelet transforms such as the discrete wavelet transform...
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Time Series
A time series is a collection of data points ordered chronologically and recorded at successive time intervals. These data points can be taken over...
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Financial Time Series and Related Models
Financial time series analysis has been one of the hottest research topics in the recent decades. In this chapter, we illustrate the stylized facts...
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Time Series
Time series refers to any group of statistical information accumulated at regular intervals. It is a quantitative method used to determine patterns...
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Entropy-based fuzzy clustering of interval-valued time series
This paper proposes a fuzzy C -medoids-based clustering method with entropy regularization to solve the issue of grou** complex data as...
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Dimension reduction and visualization of multiple time series data: a symbolic data analysis approach
Exploratory analysis and visualization of multiple time series data are essential for discovering the underlying dynamics of a series before...
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Test for conditional quantile change in general conditional heteroscedastic time series models
This study aims to test for detecting a change point in the conditional quantile of general location-scale time series models. This issue is quite...
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Time Series Models
This textbook provides a self-contained presentation of the theory and models of time series analysis. Putting an emphasis on weakly stationary...
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Estimating weak periodic vector autoregressive time series
This article develops the asymptotic distribution of the least squares estimator of the model parameters in periodic vector autoregressive time...
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Quadratic Prediction of Time Series via Auto-Cumulants
Nonlinear prediction of time series can offer potential accuracy gains over linear methods when the process is nonlinear. As there are numerous...
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Fuzzy clustering of time series based on weighted conditional higher moments
This paper proposes a new approach to fuzzy clustering of time series based on the dissimilarity among conditional higher moments. A system of...
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Probabilistic Forecasting of Seasonal Time Series
In this article, we propose a framework for seasonal time series probabilistic forecasting. It aims at forecasting (in a probabilistic way) the whole...
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Information Extraction: Non-time Series Methods
I focused on time series data for predicting an outcome in Chaps. 3 and 4...
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Zero-modified count time series modeling with an application to influenza cases
The past few decades have seen considerable interest in modeling time series of counts, with applications in many domains. Classical and Bayesian...
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Zero-Inflated Time Series Clustering Via Ensemble Thick-Pen Transform
This study develops a new clustering method for high-dimensional zero-inflated time series data. The proposed method is based on thick-pen transform...
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Simultaneous Denoising and Heterogeneity Learning for Time Series Data
Noisy time series data are often collected in biomedical applications, and it remains an important task to understand the data heterogeneity. We...
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Applied Time Series Analysis and Forecasting with Python
This textbook presents methods and techniques for time series analysis and forecasting and shows how to use Python to implement them and solve data...
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Time Series Concepts and Python
In this chapter, by observing some real-life examples of time series, we will understand the concept of time series and then learn about brief...
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Nonstationary Time Series Models
This chapter focuses on the Box-Jenkins approach to building models for nonstationary time series. It contains ARIMA modeling for nonseasonal time...