An Analysis on Tesla’s Stock Price Forecasting Using ARIMA Model

Subakkar, Aishwariya; Jasmine, S. Graceline; Anbarasi, L. Jani; Ganesh, J.; Sri, C. M. Yuktha

doi:10.1007/978-981-19-2358-6_8

Cited by 2 publications

(3 citation statements)

References 6 publications

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“…The well-known traditional statistics time series forecasting methods, such as ARIMA and its variants 17,[21][22][23][24][25][26][27][28][29] are still used a lot because of their efficiency level. Table 1 presents a summary of ARIMA-based approaches for SPF.…”

Section: Arima Approachesmentioning

confidence: 99%

A bibliometric literature review of stock price forecasting: From statistical model to deep learning approach

Vuong,

Phu,

Van Nguyen

et al. 2024

Science Progress

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We introduce a comprehensive analysis of several approaches used in stock price forecasting, including statistical, machine learning, and deep learning models. The advantages and limitations of these models are discussed to provide an insight into stock price forecasting. Traditional statistical methods, such as the autoregressive integrated moving average and its variants, are recognized for their efficiency, but they also have some limitations in addressing non-linear problems and providing long-term forecasts. Machine learning approaches, including algorithms such as artificial neural networks and random forests, are praised for their ability to grasp non-linear information without depending on stochastic data or economic theory. Moreover, deep learning approaches, such as convolutional neural networks and recurrent neural networks, can deal with complex patterns in stock prices. Additionally, this study further investigates hybrid models, combining various approaches to explore their strengths and counterbalance individual weaknesses, thereby enhancing predictive accuracy. By presenting a detailed review of various studies and methods, this study illuminates the direction of stock price forecasting and highlights potential approaches for further studies refining the stock price forecasting models.

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Section: Arima Approachesmentioning

confidence: 99%

A bibliometric literature review of stock price forecasting: From statistical model to deep learning approach

Vuong,

Phu,

Van Nguyen

et al. 2024

Science Progress

View full text Add to dashboard Cite

show abstract

“…According to Petrova (2022), the ARIMA model has shown its effectiveness in working with different time series and has become a powerful tool for obtaining accurate forecasts. [7] Moreover, Subakkar et. al.…”

Section: Review Of Related Literaturementioning

confidence: 99%

“…(2023) stated in their study that ARIMA model is analyzed for time series prediction, and the results obtained using this model show strong accuracy for short term and daily stock prediction, and this engages with other model for predicting stock price. [8] Urrutia & Olfindo (2015). Their study entitled "MODELLING AND FORECASTING THE EXCHANGE RATE OF THE PHILIPPINES: A TIME SERIES ANALYSIS", formulated an ARIMA model for the 6-year forecast of Exchange Rate of the Philippines.…”

Section: Review Of Related Literaturementioning

confidence: 99%

Forecasting the Daily Stock Prices of the Philippine Stock Exchange Index During the Opening and Closing of the Market

2023

IJFMR

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An Analysis on Tesla’s Stock Price Forecasting Using ARIMA Model

Cited by 2 publications

References 6 publications

A bibliometric literature review of stock price forecasting: From statistical model to deep learning approach

A bibliometric literature review of stock price forecasting: From statistical model to deep learning approach

Forecasting the Daily Stock Prices of the Philippine Stock Exchange Index During the Opening and Closing of the Market

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