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A dimension reduction factor approach for multivariate time series with long-memory: a robust alternative method
This paper studies factor modeling for a vector of time series with long-memory properties to investigate how outliers affect the identification of...
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Time Series Procedures to Improve Extreme Quantile Estimation
Although extreme events can occur rarely, they may have significant social and economic impacts. To assess the risk of extreme events, it is...
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Frequency Domain Clustering: An Application to Time Series with Time-Varying Parameters
Time series distribution parameters, such as mean and variance, are usually used as features for clustering. In this paper, starting from the...
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Exploratory Time Series Data Analysis
This chapter conducts exploratory time series data analysis with Python. In fact we have made some exploratory data analyses by means of time series...
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Trend and cycle decomposition of Markov switching (co)integrated time series
In this paper we derive the Beveridge–Nelson (BN) decomposition and the state space representation for various multivariate (co)integrated time...
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Nonstatistical Methods for Analysis, Forecasting, and Mining Time Series
This is an overview paper, in which we briefly present results obtained over several years in the analysis, forecasting, and mining information from...
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Finite mixture of hidden Markov models for tensor-variate time series data
The need to model data with higher dimensions, such as a tensor-variate framework where each observation is considered a three-dimensional object,...
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Optimal Combination Forecast for Bitcoin Dollars Time Series
Bitcoin has been the most used blockchain platform in business and finance in recent years. This paper aims to find a reliable prediction model that...
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Applying Data Analytics and Time Series Forecasting for Thorough Ethereum Price Prediction
Finance has been combined with technology to introduce newer advances and facilities in the domain. One such technological advance is cryptocurrency...
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Financial Projections
Forecasting helps managers guide strategy and make informed decisions about critical business operations such as sales, expenses, revenue, and...
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Kolmogorov–Smirnov simultaneous confidence bands for time series distribution function
Claims about distributions of time series are often unproven assertions instead of substantiated conclusions for lack of hypotheses testing tools. In...
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Time Series Models
THE MODELS for data described so far have been concerned with independent observations on multivariate values. The data examined in this chapter are...
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Time Series Analysis
The emergence of digital technologies has been changing how things are done in the workplace, in society, and even at home. Recent technological...
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Forecasting Natural Gas Prices with Spatio-Temporal Copula-Based Time Series Models
In this work, we model and forecast commodity price time series using multivariate copula-based time series models. In particular, we consider the...
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Time Series Analysis
The time dimension of the Data Cube is a major complication you will eventually face in analyzing your business data because time is a part of most...
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Information Extraction: Basic Time Series Methods
All data share a common structure, and all models are meant to reflect that data share the same structure.
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Forecasting highly persistent time series with bounded spectrum processes
Long memory models can be generalised by the Fractional equal-root Autoregressive Moving Average (FerARMA) process, which displays short memory for a...
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The General Tail Dependence Function in the Marshall-Olkin and Other Parametric Copula Models with an Application to Financial Time Series
The accurate understanding of the dependence structure implied by the parametric models studied in statistical and financial literature has drawn...
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Portmanteau Tests for Semiparametric Nonlinear Conditionally Heteroscedastic Time Series Models
A class of multivariate time series models is considered, with general parametric specifications for the conditional mean and variance. In this...
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Diagnostic Methods in Time Series
This book contains new aspects of model diagnostics in time series analysis, including variable selection problems and higher-order asymptotics of...