Highly comparative time-series analysis - ORA - Oxford.
This thesis presents new methods in time series analysis focusing on three areas: stationarity testing, network autoregression modelling, and local white noise testing. We begin by describing a bespoke stationarity test for use when univariate data has missing observations.
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Abstract In this thesis, I study high-dimensional nonlinear time series analysis, and its applications in financial forecasting and identifying risk in highly interconnected financial networks. The first chapter is devoted to the testing for nonlinearity in financial time series.
Goodness of Fit and Lasso Variable Selection in Time Series Analysis Sohail Chand Thesis submitted to The University of Nottingham for the degree of Doctor of Philosophy January, 2011 Dedicated to my mother and the memory of my father! I love my father as the stars - he’s a bright shining example and a happy twinkling in my heart.
PhD Thesis by Christophe Gisler: time series represent a large part of the data supply worldwide and many data mining tasks, such as prediction and classification, are concerned with them. This thesis focused on the analysis and development of generic machine learning approaches to multivariate time series classification.
This thesis deals with different topics in time series econometrics that belong, broadly speaking, to the area of macroeconometrics. That is, topics and methods are investigated which are of interest to applied researchers that want to analyze the behavior of aggregate measurements of the economy by means of time series data.
Kendall trend detection test, time-series analysis over a 30-year period revealed a significant increase in winter and autumn precipitation and a decrease in summer precipitation. The analysis of flow time-series indicated an increase in winter and July flows and a decrease in spring flows. Changes in climate variability over the same period.