Optimizing LSTM for time series prediction in Indian stock market

Yadav, Anita; Jha, C. K.; Sharan, Aditi

doi:10.1016/j.procs.2020.03.257

Cited by 256 publications

(102 citation statements)

References 13 publications

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“…The performance of the LSTM is highly dependent on selecting the hyper-parameters for achieving good results ( Yadav et al, 2020 ). Long Short-Term Memory is used for handling the sequences in the input observations and is capable of learning mapping of input to output functions which are not supported by MLP and CNN.…”

Section: Deep Learning-based Time Series Forecastingmentioning

confidence: 99%

Forecasting of COVID-19 cases using deep learning models: Is it reliable and practically significant?

Devaraj¹,

et al. 2021

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Section: Deep Learning-based Time Series Forecastingmentioning

confidence: 99%

Forecasting of COVID-19 cases using deep learning models: Is it reliable and practically significant?

Devaraj¹,

et al. 2021

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“…In [3] Budiharto propose an LSTMbased approach for stock price forecasting in Indonesia. Yadav et al [4] propose an optimized LSTM for Indian stock market forecasts. The Sri Lanka market was the subject of an RNN model proposal by Samarawickrama et al in [5].…”

Section: Related Workmentioning

confidence: 99%

An LSTM and GRU based Trading Strategy Adapted to the Moroccan Market

Touzani

Douzi

2021

Preprint

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Forecasting stock prices is an extremely challenging job considering the high volatility and the number of variables that influence it (political, economical, social, etc.). Predicting the closing price provides useful information and helps the investor to make the right decision. The use of deep learning and more precisely the recurrent neural networks RNNs in stock market forecasting is an increasingly common practice in the literature. The Long Short Term Memory LSTM and Gated Recurrent Unit GRU architectures are among the most widely used types of RNN networks, given their suitability for sequential data. In this paper, we propose a trading strategy designed for the Moroccan stock market, based on two deep learning model: Long Short Term Memory LSTM and Gated Recurrent Unit GRU to predict respectively close price for short and mid term horizon. Decision rules for buying and selling stocks are implemented based on the forecasting given by the two models, then over four three-years periods, we simulate transactions using these decision rules with different parameters for each stock. We only hold stocks that ensure a return greater than a benchmark return over the four periods. Random search is then used to choose one of the available parameters and the performance of the portfolio built from the selected stocks will be tested over a further period. The repetition of this process with a variation of benchmark return makes it possible to select the best possible combination of stock each with the parameters optimized for the decision rules. The proposed strategy produces very promising results and outperform the performance of indices used as benchmarks in the local market.

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“…Namun RNN mempunyai masalah vanishing dan exploding gradient yaitu apabila terjadi perubahan pada jangkauan nilai dari satu lapisan menuju lapisan berikutnya pada sebuah arsitektur. LSTM dibangun dan dirancang untuk mengatasi masalah gradien menghilang dari RNN ketika berhadapan dengan vanishing dan Title of manuscript is short and clear, implies research results (First Author) exploding gradient tersebut [10]. Arsitektur LSTM terdiri dari lapisan input, lapisan output, dan lapisan tersembunyi yang disajikan pada Gambar 2.…”

Section: Pembuatan Modelunclassified

Prediksi Rerata Harga Beras Tingkat Grosir Indonesia dengan Long Short Term Memory

sanjaya¹,

Heksaputra²

2020

JATISI

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Fluktuasi harga bahan pokok khususnya harga beras yang tidak terkendali berimbas pada kerugian bagi produsen dan konsumen. Untuk dapat menjembatani permasalahan tersebut maka dibutuhkan pengambilan keputusan yang tepat. Prediksi adalah salah satu unsur yang dapat digunakan dalam mendukung pengambilan keputusan yang tepat. Prediksi dalam pengambilan keputusan didasarkan pada data yang ada pada waktu sekarang dan waktu lampau sehingga mampu digunakan untuk menggambarkan kondisi yang sesuai dengan sasaran yang ingin dicapai. Dengan prediksi harga beras yang akurat maka diharapkan pengambil keputusan mampu memutuskan kebijakan yang baik atau melakukan tindakan preventif untuk meminimalkan kerugian. Pada studi ini mengkaji prediksi harga beras ditingkat grosir indonesia dengan pendekatan Recurrent Neural Network Long Short Term Memory (RNN LSTM). Dalam studi ini data yang digunakan adalah data Rata-rata Harga Beras di Tingkat Perdagangan Besar/Grosir Indonesia tahun 2010-2020 yang didapat dari badan pusat statistic Indonesia. Hasil yang didapat menunjukkan bahwa metode LSTM mampu digunakan untuk memprediksi harga beras di tingkat grosir Indonesia dengan cukup baik

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Optimizing LSTM for time series prediction in Indian stock market

Cited by 256 publications

References 13 publications

Forecasting of COVID-19 cases using deep learning models: Is it reliable and practically significant?

Forecasting of COVID-19 cases using deep learning models: Is it reliable and practically significant?

An LSTM and GRU based Trading Strategy Adapted to the Moroccan Market

Prediksi Rerata Harga Beras Tingkat Grosir Indonesia dengan Long Short Term Memory

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