Time Series Data Analysis for Stock Market Prediction

Kulkarni, Mugdha Shailendra; Jadha, Anil; Dhingra, Deepika

doi:10.2139/ssrn.3563111

Cited by 12 publications

(6 citation statements)

References 14 publications

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“…Besides, in this study, the data used are specific to some S&P 500 companies, ranging from 3 January 2006 until 18 September 2020, contrary to what is observed in the literature, where the time range is typically smaller and no designation of the companies is madealthough, some comparisons and findings can be discerned. In [13], the SMA network obtained a MAPE of 11.45% for only a short period of a year, a value worse than what was obtained in the present study for any company for the whole time period available on the datasets. The other two studies presented previously on Section 2, [12,14], related to the The results obtained in this experiment were very promising, showing that the HTM theory provides a solid framework for time series forecasting, achieving good predictions with few data.…”

Section: Resultscontrasting

confidence: 99%

“…In the same year, in [13], a LSTM network was also used, using an S&P 500 data set for the period from 17 December 2010 to 17 January 2013. In the published document, the objective was well clarified, and it was intended to predict the value of the following day, based on the last 30 days; the mean absolute percentage error (MAPE) obtained was 0.0410%.…”

Section: State Of the Artmentioning

confidence: 99%

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Hierarchical Temporal Memory Theory Approach to Stock Market Time Series Forecasting

et al. 2021

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Over the years, and with the emergence of various technological innovations, the relevance of automatic learning methods has increased exponentially, and they now play a key role in society. More specifically, Deep Learning (DL), with the ability to recognize audio, image, and time series predictions, has helped to solve various types of problems. This paper aims to introduce a new theory, Hierarchical Temporal Memory (HTM), that applies to stock market prediction. HTM is based on the biological functions of the brain as well as its learning mechanism. The results are of significant relevance and show a low percentage of errors in the predictions made over time. It can be noted that the learning curve of the algorithm is fast, identifying trends in the stock market for all seven data universes using the same network. Although the algorithm suffered at the time a pandemic was declared, it was able to adapt and return to good predictions. HTM proved to be a good continuous learning method for predicting time series datasets.

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Section: Resultscontrasting

confidence: 99%

Section: State Of the Artmentioning

confidence: 99%

Hierarchical Temporal Memory Theory Approach to Stock Market Time Series Forecasting

et al. 2021

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“…Ruchir et al [15] and [16] performed stock market prediction using 10 years Bombay Stock Exchange data. They have used ARIMA, Simple Moving Average(SMA) and Holt-Winters models.…”

Section: Literature Reviewmentioning

confidence: 99%

Profit Prediction Using ARIMA, SARIMA and LSTM Models in Time Series Forecasting: A Comparison

2022

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Time series forecasting using historical data is significantly important nowadays. Many fields such as finance, industries, healthcare, and meteorology use it. Profit analysis using financial data is crucial for any online or offline businesses and companies. It helps understand the sales and the profits and losses made and predict values for the future. For this effective analysis, the statistical methods-Autoregressive Integrated Moving Average (ARIMA) and Seasonal ARIMA models (SARIMA), and deep learning method-Long Short-Term Memory (LSTM) Neural Network model in time series forecasting have been chosen. It has been converted into a stationary dataset for ARIMA, not for SARIMA and LSTM. The fitted models have been built and used to predict profit on test data. After obtaining good accuracies of 93.84% (ARIMA), 94.378% (SARIMA) and 97.01% (LSTM) approximately, forecasts for the next 5 years have been done. Results show that LSTM surpasses both the statistical models in constructing the best model.

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“…After evaluation, the system got 97% accuracy. [6] proposed the best fit model for 10 years of data stock exchange. The major objective was to determine a pattern of the stock market trend for historical data.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Various metrics such as RMSE, directional accuracy, precision, recall and F1-measure were used to measure the performance. To predict the future closing price, artificial neural networks (ANN) and random forest (RF) were used [6]. ANN is one of the techniques from machine/ deep learning based on a network of perceptrons (neurons) that exchanges data with each other and learn fundamental trends/patterns from data using backpropagation and then generalize from data for future prediction.…”

Section: Literature Reviewmentioning

confidence: 99%

Data Analysis, Visualization and Prediction of Stock Market Prices of K-Electric

Islam

Afzal

Wali

et al. 2022

PakJET

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Predicting stock price is a trend yet very challenging task. It is because the stock prices depend upon several internal and external factors. Stock price prediction can be very useful for financial sectors and the government and help in informed decision-making. This paper analyzes the stock market prices of K-Electric Karachi. It is found that the stock prices of K-electric depend on the stock prices of the refinery sector. The paper analyzes the stock price data of the two sectors. Also, the paper compares the stock price prediction based on moving average, auto-regressive integrated moving average (ARIMA), convolutional neural network and long short-term memory (LSTM) model. It is found that ARIMA outperforms the other algorithms. A set of experiments were conducted to test the performance of algorithms. The algorithms were analyzed based on different metrics such as root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE).

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Time Series Data Analysis for Stock Market Prediction

Cited by 12 publications

References 14 publications

Hierarchical Temporal Memory Theory Approach to Stock Market Time Series Forecasting

Hierarchical Temporal Memory Theory Approach to Stock Market Time Series Forecasting

Profit Prediction Using ARIMA, SARIMA and LSTM Models in Time Series Forecasting: A Comparison

Data Analysis, Visualization and Prediction of Stock Market Prices of K-Electric

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