The Prediction of Gold Price Using ARIMA Model

Yang, Xiaohui

doi:10.2991/ssphe-18.2019.66

Cited by 27 publications

(26 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The testing error values of proposed rough set based affinity propagation model is compared to rough set and recently worked ARIMA model [30] which are depicted below in The prediction accuracy of opening price of gold resulted by proposed model and two aforesaid models are analyzed here. The RMSE of 1 day prediction by proposed model is 0.57 where as rough set based and ARIMA models generate 1.545 and 0.99 respectively.…”

Section: Simulated Results Analysismentioning

confidence: 99%

Rough Set Based Affinity Propagation Model for Prediction of Future Gold Price in Indian Scenario

Nayak*,

Teja,

Tripathy

et al. 2019

IJITEE

View full text Add to dashboard Cite

In international market, trading of metals has played a vital role. Metal cost might affect the nation’s economy. There are so many base metals available which have been utilized in world trading for construction and manufacturing of goods. Among them gold, silver, platinum, palladium have been treated as precious metals which has economic values. Therefore today’s researchers have concentrated their investigation on metal prediction using diversified algorithms like Auto Regressive Integrated Moving Average (ARIMA), KNN (K-Nearest Neighbor),Artificial Neural Network (ANN) and Support Vector Machine (SVM) etc. In this paper our foremost objective is to predict gold price, so we put our research on this metal. In this work we have employed rough set based affinity propagation algorithm for predicting future gold price and we compared our proposed model with rough set and ARIMA model basing upon the performance measures such as root mean square error (RMSE) and mean absolute percentage error (MAPE). The experimental result shows that the proposed model outperforms rough set and ARIMA model

show abstract

Section: Simulated Results Analysismentioning

confidence: 99%

Rough Set Based Affinity Propagation Model for Prediction of Future Gold Price in Indian Scenario

Nayak*,

Teja,

Tripathy

et al. 2019

IJITEE

View full text Add to dashboard Cite

show abstract

“…In the arrangement of observed data characterized by the time series over the time period, the ARIMA model is one of the models that have capabilities to present stationary data as well as nonstationary time series data. ARIMA models can thus deliver precise predictions based on the historical data of single variables [20], [21]. The approach of the Box-Jenkins methodology is used to construct ARIMA models based on initial data investigation, model identification, model validation and checking and selection of the model's use.…”

Section: Methodsmentioning

confidence: 99%

Prediction of Hotspots in Riau Province, Indonesia Using the Autoregressive Integrated Moving Average (ARIMA) Model

Kadir¹,

Dayana²,

Rosa³

et al. 2020

SAR Journal

View full text Add to dashboard Cite

Various forms of disasters occur worldwide, one of which is fire. Indonesia has been suffering from frequent land and forest fires. These events are not a new phenomenon and seem to be an annual tradition, especially in the dry season. This nation was most affected by an excessively disastrous forest fire in 2015. The misfortunes suffered were massive and resulted in land and forest damage that may have great economic and environmental costs. One solution to reduce the impacts of such events is to predict the emergence of hotspots. Therefore, in this work, a modeling method using time series produced by the Box-Jenkins' Autoregressive Integrated Moving Average (ARIMA) model was used to predict the appearance of hotspots. Since the forecasting system does not expect any detailed form to be predicted in terms of the time series of historical data, the data demonstrated in the proposed model were different from data from other models used for prediction. The study was conducted based on monthly hotspot occurrence data from January 2014 through June 2019 in Riau Province, Indonesia. The data were downloaded from the collection of the "LAPAN-MODIS-Catalog". Based on the results shown, the Autoregressive Integrated Moving Average (ARIMA) model (2,1,2) produced good predictions based on its lowest value of Mean Squared Error (MSE), 9540.088. Moreover, the proposed model has produced highly accurate forecasts of hotspots for time periods of up to five months using the fitting model of ARIMA (2,1,2), and the values forecasted for 5 months ahead were 25, 31, 26, 30 and 27.

show abstract

“…The theoretical background of ARIMA is explained in [97]. Some studies have exemplified the applicability of ARIMA in forecasting time-series in different domains such as cryptocurrencies [13], stock [14][15][16], rubber and latex [17,18], agricultural and e-commerce products [19][20][21]27], gold [22,23], and electricity [24][25][26] from 2003 to 2019 and demonstrated exceptionally promising results. From the studies made, ARIMA has difficulty when handling non-stationary time-series data, as it is trying to model the changes based on the historical time-series data with linearity assumptions when performing forecasting.…”

Section: Autoregressive Integrated Moving Average (Arima)mentioning

confidence: 99%

“…From the studies made, ARIMA has difficulty when handling non-stationary time-series data, as it is trying to model the changes based on the historical time-series data with linearity assumptions when performing forecasting. It is noticed that the following factors will affect the model performance: sudden fluctuation [23], nonlinear and non-stationary time-series data [21], and multi-step ahead forecasting [98]. There are other variations of ARIMA introduced, such as ARIMA with the independent variable (ARIMAX) [17] and Seasonal ARIMA (SARIMA) [18] for multivariate modeling and seasonal time-series data problems, respectively.…”

Section: Autoregressive Integrated Moving Average (Arima)mentioning

confidence: 99%

Exploring Time-Series Forecasting Models for Dynamic Pricing in Digital Signage Advertising

et al. 2021

View full text Add to dashboard Cite

Audience attention is vital in Digital Signage Advertising (DSA), as it has a significant impact on the pricing decision to advertise on those media. Various environmental factors affect the audience attention level toward advertising signage. Fixed-price strategies, which have been applied in DSA for pricing decisions, are generally inefficient at maximizing the potential profit of the service provider, as the environmental factors that could affect the audience attention are changing fast and are generally not considered in the current pricing solutions in a timely manner. Therefore, the time-series forecasting method is a suitable pricing solution for DSA, as it improves the pricing decision by modeling the changes in the environmental factors and audience attention level toward signage for optimal pricing. However, it is difficult to determine an optimal price forecasting model for DSA with the increasing number of available time-series forecasting models in recent years. Based on the 84 research articles reviewed, the data characteristics analysis in terms of linearity, stationarity, volatility, and dataset size is helpful in determining the optimal model for time-series price forecasting. This paper has reviewed the widely used time-series forecasting models and identified the related data characteristics of each model. A framework is proposed to demonstrate the model selection process for dynamic pricing in DSA based on its data characteristics analysis, paving the way for future research of pricing solutions for DSA.

show abstract

The Prediction of Gold Price Using ARIMA Model

Cited by 27 publications

References 3 publications

Rough Set Based Affinity Propagation Model for Prediction of Future Gold Price in Indian Scenario

Rough Set Based Affinity Propagation Model for Prediction of Future Gold Price in Indian Scenario

Prediction of Hotspots in Riau Province, Indonesia Using the Autoregressive Integrated Moving Average (ARIMA) Model

Exploring Time-Series Forecasting Models for Dynamic Pricing in Digital Signage Advertising

Contact Info

Product

Resources

About