A non-parametric model for fuzzy forecasting time series data

2021 ◽  
Vol 40 (4) ◽  
Author(s):  
Gholamreza Hesamian ◽  
Mohammad Ghasem Akbari
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Parametric Model ◽  
Series Data ◽  
Non Parametric

scholarly journals Jonckheere–Terpstra–Kendall-based non-parametric analysis of temporal differential gene expression

2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Hitoshi Iuchi ◽  
Michiaki Hamada
Keyword(s):  
Gene Expression ◽  
Time Series ◽  
Time Course ◽  
Time Series Data ◽  
Expression Patterns ◽  
Detection Methods ◽  
Series Data ◽  
Expression Levels ◽  
Over Time ◽  
Non Parametric

Abstract Time-course experiments using parallel sequencers have the potential to uncover gradual changes in cells over time that cannot be observed in a two-point comparison. An essential step in time-series data analysis is the identification of temporal differentially expressed genes (TEGs) under two conditions (e.g. control versus case). Model-based approaches, which are typical TEG detection methods, often set one parameter (e.g. degree or degree of freedom) for one dataset. This approach risks modeling of linearly increasing genes with higher-order functions, or fitting of cyclic gene expression with linear functions, thereby leading to false positives/negatives. Here, we present a Jonckheere–Terpstra–Kendall (JTK)-based non-parametric algorithm for TEG detection. Benchmarks, using simulation data, show that the JTK-based approach outperforms existing methods, especially in long time-series experiments. Additionally, application of JTK in the analysis of time-series RNA-seq data from seven tissue types, across developmental stages in mouse and rat, suggested that the wave pattern contributes to the TEG identification of JTK, not the difference in expression levels. This result suggests that JTK is a suitable algorithm when focusing on expression patterns over time rather than expression levels, such as comparisons between different species. These results show that JTK is an excellent candidate for TEG detection.

scholarly journals A Self-Calibrated Non-Parametric Time Series Analysis Approach for Assessing Insect Defoliation of Broad-Leaved Deciduous Nothofagus pumilio Forests

2019 ◽  
Vol 11 (2) ◽  
pp. 204 ◽  
Author(s):  
Roberto Chávez ◽  
Ronald Rocco ◽  
Álvaro Gutiérrez ◽  
Marcelo Dörner ◽  
Sergio Estay
Keyword(s):  
Time Series ◽  
Vegetation Index ◽  
Time Series Data ◽  
Natural Forest ◽  
Series Data ◽  
True Anomaly ◽  
Nothofagus Pumilio ◽  
Phenological Cycle ◽  
Parametric Functions ◽  
Non Parametric

Folivorous insects cause some of the most ecologically and economically important disturbances in forests worldwide. For this reason, several approaches have been developed to exploit the temporal richness of available satellite time series data to detect and quantify insect forest defoliation. Current approaches rely on parametric functions to describe the natural annual phenological cycle of the forest, from which anomalies are calculated and used to assess defoliation. Quantification of the natural variability of the annual phenological baseline is limited in parametric approaches, which is critical to evaluating whether an observed anomaly is “true” defoliation or only part of the natural forest variability. We present here a fully self-calibrated, non-parametric approach to reconstruct the annual phenological baseline along with its confidence intervals using the historical frequency of a vegetation index (VI) density, accounting for the natural forest phenological variability. This baseline is used to calculate per pixel (1) a VI anomaly per date and (2) an anomaly probability flag indicating its probability of being a “true” anomaly. Our method can be self-calibrated when applied to deciduous forests, where the winter VI values are used as the leafless reference to calculate the VI loss (%). We tested our approach with dense time series from the MODIS enhanced vegetation index (EVI) to detect and map a massive outbreak of the native Ormiscodes amphimone caterpillars which occurred in 2015–2016 in Chilean Patagonia. By applying the anomaly probability band, we filtered out all pixels with a probability <0.9 of being “true” defoliation. Our method enabled a robust spatiotemporal assessment of the O. amphimone outbreak, showing severe defoliation (60–80% and >80%) over an area of 15,387 ha of Nothofagus pumilio forests in only 40 days (322 ha/day in average) with a total of 17,850 ha by the end of the summer. Our approach is useful for the further study of the apparent increasing frequency of insect outbreaks due to warming trends in Patagonian forests; its generality means it can be applied in deciduous broad-leaved forests elsewhere.

KOMPOS: Connecting Causal Knots in Large Nonlinear Time Series with Non-Parametric Regression Splines

2021 ◽  
Vol 12 (5) ◽  
pp. 1-27
Author(s):  
Georgios Koutroulis ◽  
Leo Botler ◽  
Belgin Mutlu ◽  
Konrad Diwold ◽  
Kay Römer ◽  
...  
Keyword(s):  
Time Series ◽  
Real World ◽  
Time Series Data ◽  
Causal Structure ◽  
Multivariate Adaptive Regression Splines ◽  
Stationary Time Series ◽  
Causal Discovery ◽  
Series Data ◽  
Regression Splines ◽  
Non Parametric

Recovering causality from copious time series data beyond mere correlations has been an important contributing factor in numerous scientific fields. Most existing works assume linearity in the data that may not comply with many real-world scenarios. Moreover, it is usually not sufficient to solely infer the causal relationships. Identifying the correct time delay of cause-effect is extremely vital for further insight and effective policies in inter-disciplinary domains. To bridge this gap, we propose KOMPOS, a novel algorithmic framework that combines a powerful concept from causal discovery of additive noise models with graphical ones. We primarily build our structural causal model from multivariate adaptive regression splines with inherent additive local nonlinearities, which render the underlying causal structure more easily identifiable. In contrast to other methods, our approach is not restricted to Gaussian or non-Gaussian noise due to the non-parametric attribute of the regression method. We conduct extensive experiments on both synthetic and real-world datasets, demonstrating the superiority of the proposed algorithm over existing causal discovery methods, especially for the challenging cases of autocorrelated and non-stationary time series.

scholarly journals The Decomposition and Forecasting of Mutual Investment Funds Using Singular Spectrum Analysis

Entropy ◽  
2020 ◽  
Vol 22 (1) ◽  
pp. 83 ◽  
Author(s):  
Paulo Canas Rodrigues ◽  
Jonatha Pimentel ◽  
Patrick Messala ◽  
Mohammad Kazemi
Keyword(s):  
Time Series ◽  
Spectrum Analysis ◽  
Time Series Data ◽  
Singular Spectrum Analysis ◽  
Model Fit ◽  
Series Data ◽  
Computational Time ◽  
Investment Funds ◽  
Singular Spectrum ◽  
Non Parametric

Singular spectrum analysis (SSA) is a non-parametric method that breaks down a time series into a set of components that can be interpreted and grouped as trend, periodicity, and noise, emphasizing the separability of the underlying components and separate periodicities that occur at different time scales. The original time series can be recovered by summing all components. However, only the components associated to the signal should be considered for the reconstruction of the noise-free time series and to conduct forecasts. When the time series data has the presence of outliers, SSA and other classic parametric and non-parametric methods might result in misleading conclusions and robust methodologies should be used. In this paper we consider the use of two robust SSA algorithms for model fit and one for model forecasting. The classic SSA model, the robust SSA alternatives, and the autoregressive integrated moving average (ARIMA) model are compared in terms of computational time and accuracy for model fit and model forecast, using a simulation example and time series data from the quotas and returns of six mutual investment funds. When outliers are present in the data, the simulation study shows that the robust SSA algorithms outperform the classical ARIMA and SSA models.

Graphical Exploratory Data Analysis for Categorical Longitudinal and Time Series Data

2013 ◽  
Author(s):  
Stephen J. Tueller ◽  
Richard A. Van Dorn ◽  
Georgiy Bobashev ◽  
Barry Eggleston
Keyword(s):  
Time Series ◽  
Data Analysis ◽  
Exploratory Data Analysis ◽  
Time Series Data ◽  
Series Data ◽  
Exploratory Data
Sign in / Sign up

Export Citation Format

Share Document