scholarly journals Time series analysis applicability on the forecast of currency in circulation in Nigeria using moving average model MA (q)

2020 ◽  
Vol 9 (2) ◽  
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Moving Average ◽  
Average Model ◽  
Series Analysis ◽  
Moving Average Model

Health Prognostic Method Based on the Time Series Analysis for Actuators

2013 ◽  
Vol 373-375 ◽  
pp. 329-332 ◽  
Author(s):  
Jing Kai Zhang ◽  
Juan Wang ◽  
Xiao Xiong Liu ◽  
Wei Guo Zhang
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Flight Control ◽  
Moving Average ◽  
Autoregressive Moving Average ◽  
Health State ◽  
Future Health ◽  
Series Analysis ◽  
Autoregressive Moving Average Model ◽  
Moving Average Model

The purpose of health prognostic is to predict the future health status of system and determine the time from the current health state to functional failure completely. Application data time series analysis method often can get the expected prediction effect. Taking into account the failure characteristics of the actuators in flight control system, the autoregressive moving average model is introduced to health prognostic. The prognostic model is established. The simulation results show the effectiveness of the algorithm.

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.

Time Series Analysis for Crime Forecasting Using ARIMA (Autoregressive Integrated Moving Average) Model

2021 ◽  
pp. 158-198
Author(s):  
Neetu Faujdar ◽  
Anant Joshi
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Data Visualization ◽  
Moving Average ◽  
Complex Data ◽  
Autoregressive Integrated Moving Average ◽  
Related Data ◽  
Series Analysis ◽  
Internet Users ◽  
Moving Average Model

With massive advancements in the fields of data analysis and data mining, a new importance has been gained by data visualization. Data visualization focuses on visualizing and abstracting complex data to make it comprehensible and easy to understand using visual representation of information. Analysis of crime and crime-related data has been steadily popularizing over the last decade, and this chapter aims at visualizing such data. Crime data for several different types of crime for many countries in the world has been collected, compiled, processed, analyzed, and visualized in this chapter. Predictive analysis of this data has also been performed using time series analysis. This chapter aims to create a hub where internet users can easily view and interpret this data.

scholarly journals Forecasting of Economic Indicators (Production, Consumption, Population) of Wheat Crop (A Case Study)

2021 ◽  
Vol 8 (6) ◽  
pp. 38-46
Author(s):  
Mohammad Karim Ahmadzai
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Moving Average ◽  
Arima Model ◽  
Wheat Crop ◽  
Series Analysis ◽  
The People ◽  
Self Sufficiency ◽  
Univariate Time Series ◽  
Short Run

Wheat is the most important food crop in Afghanistan, whether consumed by the bulk of the people or used in various sectors. The problem is that Afghanistan has a significant shortfall of wheat between domestic production and consumption. Thus, the present study looks at the issue of meeting self-sufficiency for the whole population due to wheat shortages. To do so, we employ time series analysis, which can produce a highly exact short-run prediction for a significant quantity of data on the variables in question. The ARIMA models are versatile and widely utilised in univariate time series analysis. The ARIMA model combines three processes: I the auto-regressive (AR) process, (ii) the differencing process, and (iii) the moving average (MA) process. These processes are referred to as primary univariate time series models in statistical literature and are widely employed in various applications. Where predicting future wheat requirements is one of the most important tools that decision-makers may use to assess wheat requirements and then design measures to close the gap between supply and consumption. The present study seeks to forecast Production, Consumption, and Population for the period 2002-2017 and estimate the values of these variables between 2002 and 2017. (2018-2030).  

Sign in / Sign up

Export Citation Format

Share Document