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ARIMA model
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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
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This article uses the ARIMA model to forecast equity risk of listed telecommunications and technology companies in Vietnam, based on quarterly data of listed telecommunications technology companies in the period 2011 - 2023. The results show that the ARIMA Model (1,1,1) is the most suitable model and predicts the beta of the listed telecommunications industry. From there, the author makes comments on the beta forecast results of technology and telecommunications companies to help investors limit equity risk.
10p
longtimenosee09
08-04-2024
5
2
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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
2
1
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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
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This study applies ARIMA model in forecasting the number of containers through seaport cluster No. 5 according to Decision No. 1745 / QD-BGTVT dated August 3, 2011. Data were collected from the Vietnam Maritime Administration, the Vietnam Port Association (VPA) for the period 1995-2020 and processed using the software "R".
12p
visherylsandberg
18-05-2022
9
1
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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
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The automated collection of non-specific data from livestock, combined with techniques for data mining and time series analyses, facilitates the development of animal health syndromic surveillance (AHSyS). An example of AHSyS approach relates to the monitoring of bovine fallen stock.
13p
vigalileogalilei
28-02-2022
17
1
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This study found that the proposed method produced better forecast accuracy than the other three classic methods and the hybrid EMD-ARIMA models.
26p
spiritedaway36
28-11-2021
8
1
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The present study was conducted for forecasting salinity intrusion in Ham Luong River, Ben Tre Province in 2020. The ARIMA(0,1,1)x(0,1,1)23 with constant was designed as the appropriate model for time series modeling and forecasting. Results showed that the salinity concentration increased from January to March and then decreased from April to June.
5p
viwendy2711
05-10-2021
12
1
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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
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At present, the continuous increase of household electricity demand is strategic and crucial in electricity demand management. Household electricity consumers can play an important role in this issue. The rationalization of electricity consumption might be achieved by using an efficient Demand Response (DR) program.
17p
mynguyenha
21-07-2021
23
1
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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
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Ham Luong River is a branch of Mekong River located in Ben Tre Province, which has played a crucial role in supporting livelihoods of local residents and the province's economic development. However, the saline intrusion has been expanding in Ham Luong River, which seriously affects the productive agriculture, aquaculture, and further causes tremendous difficulties for local people's lives.
8p
larachdumlanat127
02-01-2021
9
2
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In this study, under the background of over all food security in India, an attempt have been made to analyse behavior of area, irrigated area, production and productivity of rice, wheat and maize. The descriptive statistics of area, irrigated area, production and productivity of the given crop have been analysed. For the modelling and forecasting purpose Box– Jenkins ARIMA modelling technique has been used to analyse the information from 1951 through 2015.
10p
gaocaolon9
22-12-2020
17
2
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Time series models can play an important role in disease prediction. Incidence data can be used to predict the future occurrence of disease events. Developments in modeling approaches provide an opportunity to compare different time series models for predictive power.
9p
vikentucky2711
26-11-2020
9
2
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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
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Indian Robusta Coffee has made a slot for itself in the world market, particularly for its decent blend up quality. In India production of Robusta is more i.e. around 62–65%. Indian coffee prices are often random as they are largely inclined on production, demand of coffee in domestic and world level forces, etc. In this study Hybrid ARIMA-ANN models was compared with ARIMA and ANN model to evaluate the past behaviour of a time series data, in order to make inferences about its future behaviour for Robusta species of Indian coffee.
6p
chauchaungayxua8
03-10-2020
10
1
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Prices perform a number of functions in an economic system. As allocator of resources, signalling to both producers and consumers regarding the level of agricultural production and consumption, as a distributor of income and as an influence on capital formation.Box-Jenkins ARIMA model is used to forecast the chilli price in selected markets for six months.
6p
nguaconbaynhay7
15-08-2020
10
2
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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
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Forecasting of area/yield/production of crops is one of the important aspects in agricultural sector. Crop yield forecasts are extremely useful in formulation of policies regarding stock, distribution and supply of agricultural produce to different areas in the country. In this study the forecast values of area, yield and hence production of rabi pulses are found. ARIMA method should not be used for finding the forecasted values for the testing period as this would increase the uncertainty with the end period of testing data.
8p
caygaocaolon6
30-07-2020
8
1
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