Comparative Study of ARIMA Methods for Forecasting Time Series of the Mexican Stock Exchange

Rangel-González, Javier Alberto; Frausto–Solís, Juan; González-Barbosa, Juan J.; Pazos-Rangel, Rodolfo; Fraire-Huacuja, Héctor J.

doi:10.1007/978-3-319-71008-2_34

Cited by 7 publications

(3 citation statements)

References 6 publications

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“…It was concluded that ARIMA had slightly upper hand when predicting for short-term. Various group of researchers applied various model on different stocks and indices and concluded that ARIMA predicted the price with utmost accuracy [6]. In research that used genetic algorithm with ARIMA, suggested the use of other techniques in combination with it [7].…”

Section: Literature Reviewmentioning

confidence: 99%

Designing an Interface to Forecast Stock Close Price Using ARIMA

Dixit¹,

Ahirwar²,

Saketnath³

et al. 2023

Artificial Intelligence and Communication Technologies

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Stock market prediction involves a great effect of the current market trend on the price. It is thus necessary to create a model that efficiently identifies the market trend and predicts the values with high consistency and accuracy. This paper focuses on designing a system that uses the ARIMA model to predict the values. The python flask API has been used to design the web application and tkinter API has been used to design the GUI interface. To fetch the live market data nse2r library of R and nsetools library of python has been used to fetch the past market data for a period of six years.

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Section: Literature Reviewmentioning

confidence: 99%

Designing an Interface to Forecast Stock Close Price Using ARIMA

Dixit¹,

Ahirwar²,

Saketnath³

et al. 2023

Artificial Intelligence and Communication Technologies

View full text Add to dashboard Cite

show abstract

“…Several researchers have employed ARIMA models for forecast of stock prices. As an example, authors in [4,8] describe as ARIMA can easily handle such a data format and how it is well suited for time series forecasting. One more example is represented by [21] where authors described an ARIMA model with currency as exogenous feature used to forecast commodity prices.…”

Section: Related Workmentioning

confidence: 99%

“…Similar studies using ARIMA for price forecasting have been done in [4][5][6][7][8][9][10][11]. Other works focused on the forecasting of e-commerce prices have been proposed in [12] where authors proposed LSTM neural networks to obtain better prediction performances in daily phone prices in one particular marketplace, amazon.fr.…”

Section: Introductionmentioning

confidence: 98%

Forecasting E-Commerce Products Prices by Combining an Autoregressive Integrated Moving Average (ARIMA) Model and Google Trends Data

et al. 2018

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E-commerce is becoming more and more the main instrument for selling goods to the mass market. This led to a growing interest in algorithms and techniques able to predict products future prices, since they allow us to define smart systems able to improve the quality of life by suggesting more affordable goods and services. The joint use of time series, reputation and sentiment analysis clearly represents one important approach to this research issue. In this paper we present Price Probe, a suite of software tools developed to perform forecasting on products’ prices. Its primary aim is to predict the future price trend of products generating a customized forecast through the exploitation of autoregressive integrated moving average (ARIMA) model. We experimented the effectiveness of the proposed approach on one of the biggest E-commerce infrastructure in the world: Amazon. We used specific APIs and dedicated crawlers to extract and collect information about products and their related prices over time and, moreover, we extracted information from social media and Google Trends that we used as exogenous features for the ARIMA model. We fine-estimated ARIMA’s parameters and tried the different combinations of the exogenous features and noticed through experimental analysis that the presence of Google Trends information significantly improved the predictions.

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Clothing Sales Forecast Based on ARIMA-BP Neural Network Combination Model

Dong²,

Chen

et al. 2019

2019 IEEE International Conference on Power, Intelligent Computing and Systems (ICPICS)

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Comparative Study of ARIMA Methods for Forecasting Time Series of the Mexican Stock Exchange

Cited by 7 publications

References 6 publications

Designing an Interface to Forecast Stock Close Price Using ARIMA

Designing an Interface to Forecast Stock Close Price Using ARIMA

Forecasting E-Commerce Products Prices by Combining an Autoregressive Integrated Moving Average (ARIMA) Model and Google Trends Data

Clothing Sales Forecast Based on ARIMA-BP Neural Network Combination Model

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