Towards Analysing multivariate weather/climate extremes

2021 ◽  
Author(s):  
Abdel Hannachi ◽  
Nickolay Trendafilov
Keyword(s):  
Time Series ◽  
Convex Hull ◽  
Optimization Algorithm ◽  
Climate Extremes ◽  
High Dimensional ◽  
Gridded Data ◽  
Future Perspectives ◽  
Archetypal Analysis ◽  
Univariate Time Series ◽  
Dimensional State Space

<p>Extreme analysis, via e.g., GEV, was developed to deal with univariate time series, and is very difficult to extend beyond that dimension. Here we explore a different method, the archetypal analysis, which focuses on multivariate extremes. The method seeks to approximate the convex hull in high-dimensional state space, by identifying corners representing "pure" types, i.e. archetypes. The method, encompasses, in particular, the virtues of EOFs and clustering. The method is presented with a new manifold-based optimization algorithm, and applied to a number of atmospheric fields, including SST and SLP gridded data. The application to SST, in particular, reveals important features related to SST extremes. The strengths and weaknesses of the method and possible future perspectives will be discussed.</p>

Displacement Time Series Prediction Model of Landslide Based on Phase Space Reconstruction

2014 ◽  
Vol 1065-1069 ◽  
pp. 23-26
Author(s):  
Ting Yao Jiang ◽  
Shan Shan Wang
Keyword(s):  
Time Series ◽  
Phase Space ◽  
Time Series Prediction ◽  
Phase Space Reconstruction ◽  
Support Vector ◽  
Support Vector Regression Model ◽  
Univariate Time Series ◽  
Higher Dimensional ◽  
Dimensional State Space ◽  
Phase Space Theory

In order to fully reveal information about landslide displacement, it was necessary to extend a time series to a higher-dimensional state space for the characteristic of univariate time series. However, in order to control the expansion of noise, an appropriate embedded dimension of phase space reconstruction was not the bigger the better. In this paper, based on the displacement time series of landslide, the phase space theory was used to build displacement time series matrix and the entropy theory was used to get the entropy. The embedded dimension of phase space reconstruction could be adjusted according to the change of entropy and feedback of displacement prediction error and a support vector regression model was created via the support vector machine’s learning. The application on Baijiabao landslide indicates that the proposed method achieves a high accuracy and stability of prediction.

A COMPARATIVE STUDY BETWEEN UNIVARIATE AND BIVARIATE TIME SERIES MODELS FOR CRUDE PALM OIL INDUSTRY IN PENINSULAR MALAYSIA

2020 ◽  
Vol 5 (1) ◽  
pp. 374
Author(s):  
Pauline Jin Wee Mah ◽  
Nur Nadhirah Nanyan
Keyword(s):  
Time Series ◽  
Palm Oil ◽  
Moving Average ◽  
Forecast Accuracy ◽  
Peninsular Malaysia ◽  
Time Series Models ◽  
Crude Palm Oil ◽  
Univariate Time Series ◽  
Import And Export ◽  
Bivariate Time Series

The main purpose of this study is to compare the performances of univariate and bivariate models on four time series variables of the crude palm oil industry in Peninsular Malaysia. The monthly data for the four variables, which are the crude palm oil production, price, import and export, were obtained from Malaysian Palm Oil Board (MPOB) and Malaysian Palm Oil Council (MPOC). In the first part of this study, univariate time series models, namely, the autoregressive integrated moving average (ARIMA), fractionally integrated autoregressive moving average (ARFIMA) and autoregressive autoregressive (ARAR) algorithm were used for modelling and forecasting purposes. Subsequently, the dependence between any two of the four variables were checked using the residuals’ sample cross correlation functions before modelling the bivariate time series. In order to model the bivariate time series and make prediction, the transfer function models were used. The forecast accuracy criteria used to evaluate the performances of the models were the mean absolute error (MAE), root mean square error (RMSE) and mean absolute percentage error (MAPE). The results of the univariate time series showed that the best model for predicting the production was ARIMA  while the ARAR algorithm were the best forecast models for predicting both the import and export of crude palm oil. However, ARIMA  appeared to be the best forecast model for price based on the MAE and MAPE values while ARFIMA  emerged the best model based on the RMSE value.  When considering bivariate time series models, the production was dependent on import while the export was dependent on either price or import. The results showed that the bivariate models had better performance compared to the univariate models for production and export of crude palm oil based on the forecast accuracy criteria used.

scholarly journals A Time Series Sustainability Assessment of a Partial Energy Portfolio Transition

Energies ◽  
2020 ◽  
Vol 14 (1) ◽  
pp. 141
Author(s):  
Jacob Hale ◽  
Suzanna Long
Keyword(s):  
Time Series ◽  
Electricity Generation ◽  
Sustainability Assessment ◽  
Moving Average ◽  
Time Series Prediction ◽  
State Level ◽  
Autoregressive Integrated Moving Average ◽  
Energy Transitions ◽  
Univariate Time Series ◽  
Partial Energy

Energy portfolios are overwhelmingly dependent on fossil fuel resources that perpetuate the consequences associated with climate change. Therefore, it is imperative to transition to more renewable alternatives to limit further harm to the environment. This study presents a univariate time series prediction model that evaluates sustainability outcomes of partial energy transitions. Future electricity generation at the state-level is predicted using exponential smoothing and autoregressive integrated moving average (ARIMA). The best prediction results are then used as an input for a sustainability assessment of a proposed transition by calculating carbon, water, land, and cost footprints. Missouri, USA was selected as a model testbed due to its dependence on coal. Of the time series methods, ARIMA exhibited the best performance and was used to predict annual electricity generation over a 10-year period. The proposed transition consisted of a one-percent annual decrease of coal’s portfolio share to be replaced with an equal share of solar and wind supply. The sustainability outcomes of the transition demonstrate decreases in carbon and water footprints but increases in land and cost footprints. Decision makers can use the results presented here to better inform strategic provisioning of critical resources in the context of proposed energy transitions.

A Verifiable Imputation Analysis for Univariate Time Series and Enabling Package

2020 ◽  
Author(s):  
Nianjun Zhou ◽  
Dhaval Patel ◽  
Arun Iyengar ◽  
Shrey Shrivastava ◽  
Anuradha Bhamidipaty
Keyword(s):  
Time Series ◽  
Univariate Time Series
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