Stock market predication using a linear regression

Bhuriya, Dinesh; Kaushal, Girish; Sharma, Ashish; Singh, Upendra

doi:10.1109/iceca.2017.8212716

Cited by 86 publications

(48 citation statements)

References 7 publications

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“…The paper compares the performances of the linear, polynomial, and Radial Basis Function (RBF) regression models based on the confidence values of the predicted results. In addition, Bhuriya et al (2017) reported that the linear regression model outperformed the other techniques and achieved a confidence value of 0.97.…”

Section: Statistical Approachmentioning

confidence: 98%

“…Furthermore, Ariyo et al (2014) made a solid case not to undermine the powers of ARIMA models in terms of stock analysis because it can compete reasonably well against the emerging forecasting techniques available today for short term prediction. Bhuriya et al (2017) implemented variants of regression models to predict the stock price of Tata Consultancy Services stock based on five features i.e., open, high, low, close price, and volume. The paper compares the performances of the linear, polynomial, and Radial Basis Function (RBF) regression models based on the confidence values of the predicted results.…”

Section: Statistical Approachmentioning

confidence: 99%

See 1 more Smart Citation

Stock Market Analysis: A Review and Taxonomy of Prediction Techniques

Shah

Isah

Zulkernine

2019

IJFS

314

108

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Stock market prediction has always caught the attention of many analysts and researchers. Popular theories suggest that stock markets are essentially a random walk and it is a fool’s game to try and predict them. Predicting stock prices is a challenging problem in itself because of the number of variables which are involved. In the short term, the market behaves like a voting machine but in the longer term, it acts like a weighing machine and hence there is scope for predicting the market movements for a longer timeframe. Application of machine learning techniques and other algorithms for stock price analysis and forecasting is an area that shows great promise. In this paper, we first provide a concise review of stock markets and taxonomy of stock market prediction methods. We then focus on some of the research achievements in stock analysis and prediction. We discuss technical, fundamental, short- and long-term approaches used for stock analysis. Finally, we present some challenges and research opportunities in this field.

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Section: Statistical Approachmentioning

confidence: 98%

Section: Statistical Approachmentioning

confidence: 99%

Stock Market Analysis: A Review and Taxonomy of Prediction Techniques

Shah

Isah

Zulkernine

2019

IJFS

314

108

View full text Add to dashboard Cite

show abstract

“…Regression models were initially used for prediction tasks. Bhuriya et al [40] applied linear regression methods to predict stock prices. Oteros et al [41] established a multiple regression model by using different factors of pollen concentration to take into account extreme climate events in the Mediterranean climate.…”

Section: Single Methodsmentioning

confidence: 99%

Hybrid Deep Learning Predictor for Smart Agriculture Sensing Based on Empirical Mode Decomposition and Gated Recurrent Unit Group Model

Jin

Yang

Wang

et al. 2020

Sensors

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Smart agricultural sensing has enabled great advantages in practical applications recently, making it one of the most important and valuable systems. For outdoor plantation farms, the prediction of climate data, such as temperature, wind speed, and humidity, enables the planning and control of agricultural production to improve the yield and quality of crops. However, it is not easy to accurately predict climate trends because the sensing data are complex, nonlinear, and contain multiple components. This study proposes a hybrid deep learning predictor, in which an empirical mode decomposition (EMD) method is used to decompose the climate data into fixed component groups with different frequency characteristics, then a gated recurrent unit (GRU) network is trained for each group as the sub-predictor, and finally the results from the GRU are added to obtain the prediction result. Experiments based on climate data from an agricultural Internet of Things (IoT) system verify the development of the proposed model. The prediction results show that the proposed predictor can obtain more accurate predictions of temperature, wind speed, and humidity data to meet the needs of precision agricultural production.

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“…Penjelasan atribut instant data disajikan pada Tabel 1. [14]. LSTM dapat dibawa ke dalam masalah regresi linear.…”

Section: Methodsunclassified

Prediksi Data Transaksi Penjualan Time Series Menggunakan Regresi LSTM

Ashari¹,

Sadikin

2020

j. nas. pendidik. teknik. inform.

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Sebagai upaya untuk memenangkan persaingan di pasar, perusahaan farmasi harus menghasilkan produk obat – obatan yang berkualitas. Untuk menghasilkan produk yang berkualitas, diperlukan perencanaan produksi yang baik dan efisien. Salah satu dasar perencanaan produksi adalah prediksi penjualan. PT. Metiska Farma telah menerapkan metode prediksi dalam proses produksi, akan tetapi prediksi yang dihasilkan tidak akurat sehingga menyebabkan tidak optimal dalam memenuhi permintaan pasar. Untuk meminimalisir masalah kurang akuratnya proses prediksi tersebut, dalam penelitian yang disajikan pada makalah ini dilakukan uji coba prediksi menggunakan teknik Machine Learning dengan metode Regresi Long Short Term Memory (LSTM). Teknik yang diusulkan diuji coba menggunakan dataset penjualan produk “X” dari PT. Metiska Farma dengan parameter kinerja Root Mean Squared Error (RMSE) dan MAPE (Mean Absolute Percentage Error). Hasil penelitian ini berupa nilai rata – rata evaluasi error dari pemodelan data training dan data testing. Di mana hasil menunjukan bahwa Regresi LSTM memiliki nilai prediksi penjualan dengan evaluasi model melalui RMSE sebesar 286.465.424 untuk data training dan 187.013.430 untuk data testing. Untuk nilai MAPE sebesar 787% dan 309% untuk data training dan data testing secara berurut.

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Stock market predication using a linear regression

Cited by 86 publications

References 7 publications

Stock Market Analysis: A Review and Taxonomy of Prediction Techniques

Stock Market Analysis: A Review and Taxonomy of Prediction Techniques

Hybrid Deep Learning Predictor for Smart Agriculture Sensing Based on Empirical Mode Decomposition and Gated Recurrent Unit Group Model

Prediksi Data Transaksi Penjualan Time Series Menggunakan Regresi LSTM

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