Construction of Confidence Interval for a Univariate Stock Price Signal Predicted Through Long Short Term Memory Network

De, Shankhajyoti; Dey, Arabin Kumar; Gouda, Deepak Kumar

doi:10.1007/s40745-020-00307-8

Cited by 3 publications

(2 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Azlan et al [3] conducted a time series analysis using the clonal selection algorithm and found almost similar forecasting performance as ARIMA models on yahoo stock price. In time series forecasting, LSTM models illustrated superior forecasting performance with a long-term confident band [4]. Li and Bastos [26] conducted a systematic literature review that reported LSTM is the most widely used deep learning technique on stock price forecasting.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Figure 1 illustrates the overall structure of our experiment design based on Alinma dataset and then we replicate the same procedure for the other selected datasets. A sliding window of size five is commonly used in forecasting stock price, mentioned in the surveyed literature [3], [4], [8]. As a result, the feature list contains stock prices of t-4, t-3, t-2, t-1, t days where t corresponds to today.…”

Section: Experiments Designmentioning

confidence: 99%

See 1 more Smart Citation

Impact of Hyperparameter Tuning on Machine Learning Models in Stock Price Forecasting

Hoque¹,

Aljamaan

2021

IEEE Access

View full text Add to dashboard Cite

Stock price forecasting has been reported as a challenging task in the scientific and financial communities due to stock prices' nonlinear and dynamic nature. Machine learning models exhibit capabilities that allow them to handle nonlinear data and be candidate tools for stock price forecasting. In this study, an empirical evaluation of eight conventional machine learning models' is conducted to forecast the stock price of eleven companies belonging to the Saudi Stock Exchange. Moreover, the optimal configuration of hyperparameters in each machine learning model is identified. Forecasting performance is evaluated by two well-known error metrics: Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). Wilcoxson effect size is utilized to determine the impact of hyperparameter tuning by comparing tuned and un-tuned machine learning models' forecasting performance. Empirical results indicate there are varying impacts of hyperparameter tuning of machine learning models in forecasting stock price. After tuning the hyperparameters, Support Vector Regression outperforms other forecasting models with a significant statistical difference. In contrast, Kernel Ridge Regression shows noteworthy forecasting performance without hyperparameter tuning with respect to other un-tuned forecasting models. However, Decision Tree and K-Nearest Neighbour are the poor-performing models which demonstrate inadequate forecasting performance even after hyperparameter tuning.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Section: Experiments Designmentioning

confidence: 99%