Stock prediction analysis by customers opinion in Twitter data using an optimized intelligent model

Pattewar, Tareek; Jain, Dinesh Chandra

doi:10.1007/s13278-022-00979-5

Cited by 3 publications

(1 citation statement)

References 34 publications

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“…Through unpacking the time series of incipient data, ARIMA can propose the short-term forecast correctly. Analysis of time series, in particular, ARIMA model has beis created to solve the disappearing gradient issue.en extensively used in various areas, for example economics and statistics, and it also plays an important role in medical study [4]. AR is called Autogressive model.…”

Section: Introductionmentioning

confidence: 99%

Stock Price Undulation Prediction Using ARIMA Model

2023

AEMPS

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Stock is one of the most important investments nowadays, but it is also changing very rapidly. Its dramatic changes may lead to property damage. The stock price can influence the market situation. If its future price can be predicted, peoples investments will be more accurate. To solve the problem quickly, creating models seems to be the best choice, While the influences keep changing, some easy models cannot work well. By comparison between many models, we decided to choose Auto-regressive Integrated Moving Average Model (ARIMA). ARIMA model is a set of AR model and MA model. ARIMA model uses historical information of the data to predict the future. Though ARIMA model cannot respond well to emergencies. Assume that the average month price of the stock is steady. This paper uses this model to predict whether the investor will gain or loss. Through SPSS, the model is built and tested. The models correct rate is higher than 0.7, so it can greatly help people to make decisions.

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Section: Introductionmentioning

confidence: 99%