Moving Average Models

Part 1 of ebook "Essentials of time series for financial applications" provides readers with contents including: Chapter 1 - Linear regression model; Chapter 2 - Autoregressive moving average (ARMA) models and their practical applications; Chapter 3 - Vector autoregressive moving average (VARMA) models; Chapter 4 - Unit roots and cointegration; Chapter 5 - Single-factor conditionally heteroskedastic models, ARCH and GARCH;...

244p daonhiennhien 03-07-2024 3 1   Download

This study developed a statistical model for thermal analysis to describe the variation of railway temperature that aids in detecting the potential excessive displacement in the railway efficiently. In particular, the Seasonal autoregressive integrated moving average (SARIMA) model is adopted to learn and recognize the variation patterns of railroad temperature with time.

5p dathienlang1012 03-05-2024 4 0   Download

This study compares the accuracy of two popular time series forecasting models, namely the autoregressive integrated moving average (ARIMA) model and the long short-term memory (LSTM) model, in predicting daily gold prices from 2000 to 2023. The ARIMA model is a traditional approach that relies on past values to forecast future values, while the LSTM model is a deep learning technique that captures long-term dependencies in time series data.

16p dathienlang1012 03-05-2024 0 0   Download

Time series data is a series of values observed through repeated measurements at different times. Time series data is a type of data present in almost all different fields of life. Time series prediction is an significant problem in time series data mining.

7p vilarry 01-04-2024 3 1   Download

To compare an autoregressive integrated moving average (ARIMA) model with a model that combines ARIMA with the Elman recurrent neural network (ARIMA-ERNN) in predicting the incidence of pertussis in mainland China. Background: The incidence of pertussis has increased rapidly in mainland China since 2016, making the disease an increasing public health threat.

11p viferrari 28-11-2022 8 2   Download

The study "Application of SARIMA model to forecasting the natural rubber price in the world market" was conducted to develop a forecasting model to predict the price natural rubber in the world market by using the Seasonal Autoregressive Integrated Moving Average (SARIMA).

7p tieuvulinhhoa 22-09-2022 6 3   Download

The Cryptocurrency is growing strongly and widely such as Bitcoin (BTC) and Ethereum (ETH) used in the world, which has attracted wide attention of researchers in recent times. In this work, Autoregressive Integrate Moving Average (ARIMA) model, machine learning algorithms, Support Vector Regression (SVR) will be implemented to predict the closing price of The Cryptocurrency the next day.

7p visherylsandberg 18-05-2022 14 2   Download

This study compares the price predictions of the Vanguard real estate exchangetraded fund (ETF) (VNQ) using the back propagation neural network (BPNN) and autoregressive integrated moving average (ARIMA) models. The input variables for BPNN include the past 3-day closing prices, daily trading volume, MA5, MA20, the S&P 500 index, the United States (US) dollar index, volatility index, 5-year treasury yields, and 10-year treasury yields. In addition, variable reduction is based on multiple linear regression (MLR).

16p mynguyenha 21-07-2021 18 1   Download

The current study examined the determinants of electricity consumption and also intends to forecast the electricity consumption in Pakistan. The study has used time series data analysis, applied Johansen Cointegration Test, error correction mechanisms and regression for examining determinants and autoregressive integrated moving average model is used for forecasting. The study has used times series secondary annual data on different variables for the period ranging from 1970 to 2018.

8p nguaconbaynhay10 22-02-2021 17 2   Download

This paper attempts to forecast the economic performance of Bangladesh measured with annual GDP data using an Autoregressive Integrated Moving Average (ARIMA) Model followed by test of goodness of fit using AIC (Akaike Information Criterion) and BIC (Bayesian Information Criterion) index value among six ARIMA models along with several diagnostic tests such as plotting ACF (Autocorrelation Function), PACF (Partial Autocorrelation Function) and performing Unit Root Test of the Residuals estimated by the selected forecasting ARIMA model.

20p nat_qb73 21-02-2021 11 1   Download

This paper examined the monthly modal prices of maize using Autoregressive Integrated Moving Average (ARIMA) models so as to determine the most efficient and adequate model for analyzing the maize monthly modal prices in Telangana.

9p angicungduoc8 07-11-2020 13 1   Download

Lecture 17 - Forecasting. When you complete this chapter you should be able to : Understand the three time horizons and which models apply for each use; explain when to use each of the four qualitative models; apply the naive, moving average, exponential smoothing, and trend methods.

54p koxih_kothogmih4 28-08-2020 8 1   Download

This study aims to examine the best-fitted model that allows us to forecast FNG prices more accurately in the near future. There are 2842 observed data of daily FNG prices from 2009 to 2019 as the input of study objects. The finding suggests that the first measurement model of ARIMA (1,1,1) does not fit the model as having a non-significant probability value.

7p kethamoi7 15-08-2020 11 1   Download

The present study was carried out to forecast prices of paddy through univariate Seasonal Auto Regressive Integrated Moving Average (SARIMA) model. The secondary data (Time Series data) pertaining to monthly prices (Rs/q) of paddy were collected the website of Chhattisgarh State Marketing Board (Mandi) for the period from April 2009 to February 2020 have been used for the study. The SARIMA (2,1,2) (1,1,1)12 were found as the most suitable models to forecasts of paddy prices. With these models there were made forecast for 13 months, which are from March 2020 to March 2021.

7p angicungduoc6 22-07-2020 11 1   Download

In this study, total annual exports and imports of the Kingdom of Saudi Arabia are forecasted using Artificial Neural Network (ANN) and Autoregressive Integrated Moving Average (ARIMA) models.

12p tohitohi 22-05-2020 13 0   Download

There are several linear time-series forecasting models available in literature. One of the important and widely used technique for analysis of univariate time-series data is Box Jenkins’ Autoregressive integrated moving average (ARIMA) methodology (Box et al., 2007). Sometimes addition of the other exogenous variables increases the prediction accuracy of ARIMA model (ARIMAX). For this aspect we applied different p and q order ARIMAX model for five nutrient combinations of nitrogen content which is further developed by including organic carbons an input (exogenous) variable.

9p nguaconbaynhay5 16-05-2020 23 0   Download

In this paper, three machine learning models have been applied to predict and fill in the missing monitoring data of air quality for Gia Lam and Nha Trang stations in Hanoi and Khanh Hoa respectively, including Autoregressive Moving Average (ARMA), Artificial Neural Network (ANN), and Support Vector Regression (SVR). Two air pollutants being NO2 and PM10 were selected for this study.

7p abcxyz123_02 03-03-2020 27 0   Download

Present investigation was an attempt to study the trend of jute production in West Bengal for the period starting from 1950 to 2016. For stochastic trend estimation, a number of time series parametric regression models viz. Linear model, Quadratic model, Exponential model, Logarithmic model, Power model and Auto Regressive Integrated Moving Average (ARIMA) were employed and compared for finding out an appropriate econometric model to capture the trend of jute production of the country. Based on the performance of several goodness of fit criteria viz.

12p trinhthamhodang3 17-02-2020 27 0   Download

The purpose of this paper is to study the current trend of export of sheep and goat meat from India and forecasting of the same for next five years by using the time series modelling. Four different Autoregressive Integrated Moving Average (ARIMA) models were fitted to annual sheep and goat meat export (quantity) from India using data records from 1990-1991 to 2017-18. Using goodness of fit criteria i.e.

8p trinhthamhodang3 14-02-2020 10 0   Download

The study uses autoregressive fractionally integrated moving average – fractionally integrated generalized autoregressive conditional heteroskedasticity (ARFIMA-FIGARCH) models and chaos effects to determine nonlinearity properties present on currency ETN returns. The results find that the volatilities of currency ETNs have long-memory, non-stationarity and non-invertibility properties. These findings make the research conclude that mean reversion is a possibility and that the efficient market hypothesis of Fama (1970) became ungrounded on these investment instruments.

23p trinhthamhodang2 21-01-2020 27 2   Download