scholarly journals Introduction to Statistical Data Analysis, Part II Information Criterion and Time Series Analysis

2011 ◽  
Vol 23 (1) ◽  
pp. 107-113
Author(s):  
Yoshiyasu TAMURA
Keyword(s):  
Time Series ◽  
Data Analysis ◽  
Time Series Analysis ◽  
Statistical Data ◽  
Information Criterion ◽  
Statistical Data Analysis ◽  
Series Analysis

The Deformation Prediction of Foundation Pit Slope Based on Time Series Analysis

2011 ◽  
Vol 80-81 ◽  
pp. 516-520
Author(s):  
Han Bing Liu ◽  
Yan Yi Sun ◽  
Yong Chun Cheng ◽  
Ping Jiang ◽  
Yu Bo Jiao
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Moving Average ◽  
Arma Model ◽  
Information Criterion ◽  
Slope Deformation ◽  
Foundation Pit ◽  
Deformation Prediction ◽  
Series Analysis ◽  
Moving Average Model

Slope stability is the key to ensuring the safety of foundation pit construction. This paper is on the background of metro foundation pit monitoring of the West Railway Station in Changchun City. Through the time series analysis of the pit slope deformation data, the Auto Regressive Moving Average Model (ARMA) of pit slope deformation is established. Then the orders of the model are determined by the Akaike Information Criterion (AIC). Further, the deformation prediction of pit slope is finished using the ARMA model. By the comparison of the predictive value and the true monitoring value, it shows that using time series to analyze the deformation of foundation pit slope is reasonable and reliable. At the same time, this method is providing a new way to estimate the stability of pit slope.

scholarly journals Satellite Image Time Series Analysis for Big Earth Observation Data

2021 ◽  
Vol 13 (13) ◽  
pp. 2428
Author(s):  
Rolf Simoes ◽  
Gilberto Camara ◽  
Gilberto Queiroz ◽  
Felipe Souza ◽  
Pedro R. Andrade ◽  
...  
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Data Analysis ◽  
Time Series Analysis ◽  
Satellite Image ◽  
Earth Observation ◽  
Observation Data ◽  
Accuracy Measurement ◽  
Series Analysis ◽  
Earth Observation Data

The development of analytical software for big Earth observation data faces several challenges. Designers need to balance between conflicting factors. Solutions that are efficient for specific hardware architectures can not be used in other environments. Packages that work on generic hardware and open standards will not have the same performance as dedicated solutions. Software that assumes that its users are computer programmers are flexible but may be difficult to learn for a wide audience. This paper describes sits, an open-source R package for satellite image time series analysis using machine learning. To allow experts to use satellite imagery to the fullest extent, sits adopts a time-first, space-later approach. It supports the complete cycle of data analysis for land classification. Its API provides a simple but powerful set of functions. The software works in different cloud computing environments. Satellite image time series are input to machine learning classifiers, and the results are post-processed using spatial smoothing. Since machine learning methods need accurate training data, sits includes methods for quality assessment of training samples. The software also provides methods for validation and accuracy measurement. The package thus comprises a production environment for big EO data analysis. We show that this approach produces high accuracy for land use and land cover maps through a case study in the Cerrado biome, one of the world’s fast moving agricultural frontiers for the year 2018.

scholarly journals THE ECONOMIC GROWTH FORECAST AND A REVIEW OF THE IMPACTS OF COVID-19 ON THE GHANAIAN ECONOMY: APPLICATION OF TIME SERIES ANALYSIS AND MONTE-CARLO SIMULATION

2020 ◽  
pp. 83-95
Author(s):  
Professor Li Fang Lin ◽  
Blessed Kwasi Adjei ◽  
Felix Kwame Nyarko
Keyword(s):  
Economic Growth ◽  
Time Series ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Time Series Analysis ◽  
Arima Model ◽  
Information Criterion ◽  
Growth Trend ◽  
Series Analysis ◽  
The Government

This manuscript explores the effects of Covid-19 pandemic on the economic activities of Ghana by first modelling the Economic growth figures of Ghana; discuss the current covid-19 situation and its economic impact on the nation and to wrap things up by suggesting remedial measures necessary to salvage the situation at hand. To model and forecast the Economic growth trend, the times series analysis and the Monte Carlo simulation (Laplace distribution) techniques were employed. The success of the ARIMA model was monitored through Akaike information Criterion (AIC) where irrefutably the absolute number shows the success of the model - the lower the number, the better the model. The research results showed that in spite of promising economic forecasts, with the force of the pandemic soaring universally, there is no doubt that the economic prosperity of Ghana will be disrupted and major revenue margins shrinked this year. However, due to some solid and harsh measures set out by the government we are optimistic that situations will be well contained and managed. The scientific contribution of the research lies in the fact that it will offer a new way of perceiving risks and uncertainties when policy makers are drafting budgets and economic policies going forward. In that capacity, they will not only adapt to practical and analytical methods to forecast but additionally consider some unforeseen circumstances beyond the control of humanity that may have tormenting impact on economic outputs. KEYWORDS: Time series analysis, Covid-19, Monte-Carlo simulation, GDP per Capita, Modelling, Economic Growth.

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