Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 80 for “"multivariate time series"”.
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Portmanteau Tests For Univariate And Multivariate Time Series Models
… how the number of available observations of a time series can influence its apparent stationarity as measured by two standard tests, namely the standard Dickey-Fuller (DF) test and the Augmented Dickey-Fuller (ADF) test. The univariate time series case is examined. A stationary time series …
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Model-based clustering for multivariate time series of counts
… develops a modeling framework for univariate and multivariate zero-inflated time series of counts and applies the models in a clustering scheme to identify groups of count series with similar behavior. The basic modeling framework used is observation-driven Poisson regression with generalized …
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Bayesian multivariate time series models for forecasting European macroeconomic series
… In particular, the value of joint-modelling with time-varying parameters and much more sophisticated prior distributions has been stressed in the econometric methodology literature. See e.g. Doan et al. (1984).Kadiyala and Karlsson (1993, 1997), Litterman (1986a), and Phillips (1995a, 1995b). …
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A study of kNN using ICU multivariate time series data
… of this research is the processing of ICU multivariate and high dimensional time-series data collected at irregular time periods. To handle the ICU irregular multivariate time-series three different methods were developed: Capture Statistics, Detect Changes, and Aggregate Segments. We …
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Causal Inference for Natural Language Data and Multivariate Time Series
… application areas are natural language data and multivariate time series. For text, large language models are trained on predictive tasks not necessarily well-suited for causal inference. Moreover, documents that vary in some treatment feature will often also vary systematically in other, unknown …
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Sparse Representation and its Application to Multivariate Time Series Classification
… to be sparse within a domain, such as spatial, time, or timefrequency domain, and this sparse characteristics of such signals can be obtained through the SR. The ECG signal, for instance, is typically a temporal sparse signal, comprises of various periodic activities such as time delay and …
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Sparse Representation and its Application to Multivariate Time Series Classification
… to be sparse within a domain, such as spatial, time, or timefrequency domain, and this sparse characteristics of such signals can be obtained through the SR. The ECG signal, for instance, is typically a temporal sparse signal, comprises of various periodic activities such as time delay and …
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Representation learning for multivariate time series : artefact analysis on sleep recordings
… analyses are vital to minimise diagnostic time and provide as many people as possible with the help they require. Artefact detection plays a key role in the development of automatic tools especially concerning the potential misdiagnosis by automatic models. This study explores the use of a …
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Inferring Undirected and Causally Directed Graph Structures from Multivariate Time Series
… is statistically significant. Causally related multivariate time series appear in many applications from economical systems to brain signal analysis. Inferring the causal relationships between the signals as a directed graph is the main contribution of Part Two. The directions in the inferred …
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Practical Methods in Multivariate Time Series Analysis (Causality, Arma, Box-Jenkins)
… and Jenkins initiated the burgeoning interest in time series model building over a decade ago when they developed several specialized techniques used in model selection, estimation, and checking. These methods have been widely applied by researchers interested in lag structures, forecasts, and …
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Multivariate time series clustering using kernel variant multi-way principal component analysis
Clustering multivariate time series data has been a challenging task for researchers since data has multiple dimensions to consider such as auto-correlations and cross-correlations whereas multivariate time series data has been prevailing in diverse areas for decades. However, for a short-period …
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Data-Driven Methods for Modeling and Predicting Multivariate Time Series using Surrogates
Modeling and predicting multivariate time series data has been of prime interest to researchers for many decades. Traditionally, time series prediction models have focused on finding attributes that have consistent correlations with target variable(s). However, diverse surrogate signals, such as …
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Generalized flow-based variational autoencoder networks for anomaly detection in multivariate time series
Time series are widely used in applications such as finance, robotics, telecommunications, astronomy, and many more. Detecting anomalies like robotic arm failures or server attacks is a valuable and important task. Recent research in anomaly detection in multivariate temporal data formulates the …
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Statistical inference of multivariate time series and functional data using new dependence metrics
… this thesis, we focus on inference problems for time series and functional data and develop new methodologies by using new dependence metrics which can be viewed as an extension of Martingale Difference Divergence (MDD) [see Shao and Zhang (2014)] that quantifies the conditional mean dependence of …
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Autoencoder-based multivariate time series anomaly detection and clustering for diagnosis of mechatronic systems
… also called representation, of the Autoencoder. Time windows with different lengths from multivariate time-series data as its input are used during training and inferencing of the Artificial Neural Network (ANN) for analyzing the fault detection performances’ time dependence with transient data. …
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Detecting Dependence Change Points in Multivariate Time Series with Applications in Neuroscience and Finance
… changes in the dependency structure between multivariate time series. Two examples include neuroscience and finance. The second and third chapters focus on neuroscience and introduce a data-driven technique for partitioning a time course into distinct temporal intervals with different …
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Multivariate Time-Series Deep Learning for Short-Term Forecasting of Lost Circulation in Drilling Operations
… operations, often leading to non-productive time, operational delays, and increased risk. Accurate prediction of lost circulation is challenging due to the complex, time-dependent interactions among drilling parameters, formation conditions, and operational states. This thesis investigates …
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Optimized Forecasting of Dominant U.S. Stock Market Equities Using Univariate and Multivariate Time Series Analysis Methods
… stock market equities via two very different time series analysis techniques: 1) autoregressive integrated moving average (ARIMA), and 2) singular spectrum analysis (SSA). Approximately 40% of the S&P 500 stocks are analyzed. Forecasts are generated for one and five days ahead using daily …
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Spatial-Temporal Multivariate Time Series Forecasting Using Graph Neural Networks, with an Application to Traffic Speed Prediction
Accurately forecasting complex, multivariate time series varying across space and time requires models that effectively capture spatial and temporal dependencies inherent in the data. This dissertation introduces a novel architecture based on Graph Neural Networks that addresses the challenges of …
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Multivariate Singular Spectrum Analysis: A Principled, Practical, and Performant Solution for Time Series Imputation and Forecasting
The analysis of multivariate time series data is of great interest across many domains, including cyber-physical systems, finance, retail, healthcare to name a few. A common goal across all of these domains is accurate imputation and forecasting of multivariate time series in the presence of noisy …
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