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Longitudinal Data Analysis and Time Series Commons™
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Full-Text Articles in Longitudinal Data Analysis and Time Series
Modelling And Analysis On Noisy Financial Time Series, Jinsong Leng
Modelling And Analysis On Noisy Financial Time Series, Jinsong Leng
Research outputs 2014 to 2021
Building the prediction model(s) from the historical time series has attracted many researchers in last few decades. For example, the traders of hedge funds and experts in agriculture are demanding the precise models to make the prediction of the possible trends and cycles. Even though many statistical or machine learning (ML) models have been proposed, however, there are no universal solutions available to resolve such particular prob-lem. In this paper, the powerful forward-backward non-linear filter and wavelet-based denoising method are introduced to remove the high level of noise embedded in financial time series. With the filtered time series, the statistical …
Methods For The Estimation Of Missing Values In Time Series, David S. Fung
Methods For The Estimation Of Missing Values In Time Series, David S. Fung
Theses: Doctorates and Masters
Time Series is a sequential set of data measured over time. Examples of time series arise in a variety of areas, ranging from engineering to economics. The analysis of time series data constitutes an important area of statistics. Since, the data are records taken through time, missing observations in time series data are very common. This occurs because an observation may not be made at a particular time owing to faulty equipment, lost records, or a mistake, which cannot be rectified until later. When one or more observations are missing it may be necessary to estimate the model and also …
Application Of Time Series Models To Sets Of Environmental Data, Saarah Ahmed Farag
Application Of Time Series Models To Sets Of Environmental Data, Saarah Ahmed Farag
Theses : Honours
In environmental studies, many programs have been implemented for the purpose of examining the influence of factors on the natural environment from various sources. In the past, predictions have been obtained from a simple regression model of the data from these programs to aid in deciding whether an intervention is required in the program or policy. However, most environmental time series involve correlated dependent variables which makes it difficult to apply conventional regression analysis. This thesis compares the application of the class of transfer function models, where high correlation between dependent variables is allowed, to that of conventional regression analysis. …