Communications in computer and information science, Год журнала: 2023, Номер unknown, С. 213 - 227
Опубликована: Ноя. 29, 2023
Язык: Английский
Journal of Applied Data Sciences, Год журнала: 2024, Номер 5(2), С. 326 - 341
Опубликована: Май 15, 2024
This study investigates the effectiveness of Prophet algorithm in predicting Grab Holdings' stock prices dataset from Kaggle. By meticulously analyzing historical closing, high, low, and volume data, research aims to uncover market patterns gain insights into investor sentiment based on short-term forecasting. The findings reveal a dynamic trajectory for stock, characterized by significant fluctuations evolving confidence. reached peak $14 early 2022, indicating optimism, but subsequently experienced decline $4 late 2023, reflecting shift sentiment. Notably, 2023 witnessed heightened volatility compared evident more price swings increased trading volume. demonstrated promising potential prediction better than traditional methods, which overlook presence seasonality or fail adapt conditions, leading less accurate forecasts. excellent performance is indicated Mean Absolute Percentage Error (MAPE) 10.45511%, (MAE) 3.112026, Root Squared (RMSE) 3.516969. Compared ARIMA, MAE RMSE resulting are much lower their counterparts, 14.49675 16.079898, respectively. These widely used metrics suggest moderate accuracy future prices. offers valuable investors that they can use understand trend make informed investment decisions regarding buying selling opportunities. However, it crucial acknowledge inherent limitations such models interpret results cautiously, considering ever-changing dynamics financial market.
Язык: Английский
Процитировано
0
2022 International Conference on Inventive Computation Technologies (ICICT), Год журнала: 2024, Номер unknown
Опубликована: Апрель 24, 2024
Making predictions, about stock prices has always been a challenge in research even though the Efficient Market Hypothesis suggests otherwise. While it's widely believed that impossible to predict with precision there have studies literature showing accurate predictions can be made by using predictor models and relevant variables. The complexity of predicting market volatility adds another layer difficulty as are influenced factors such conditions, psychological factors, logical considerations. In this study, Facebook Prophet model is utilized forecast analyzing data from Yahoo Finance. experimental results indicate effectively used overtime periods. work's emphasis on provides not only forecasts but also seamless visualization, allowing users gain confidence analyzing, visualizing, forecasting prices. This comprehensive effort knowledge navigate difficulties price analysis, employing automation quickly acquire solid visual insights, regardless selected stock.
Язык: Английский
Процитировано
0
WSEAS TRANSACTIONS ON BUSINESS AND ECONOMICS, Год журнала: 2024, Номер 21, С. 1611 - 1618
Опубликована: Июль 19, 2024
In the modern transport industry, vast and diverse information arrays, particularly those including time series data, are rapidly expanding. This growth presents an opportunity to improve quality of forecasting. Researchers practitioners continuously developing innovative tools predict their future values. The goal research is performance automated forecasting environments in a systematic structured way. paper investigates effect substituting initial with another similar nature, during training phase model’s development. A financial data set Prophet model employed for this objective. It observed that impact on accuracy predicted values promising, albeit not significant. Based obtained results, valuable conclusions drawn, recommendations further improvements provided. By highlighting importance incorporation, assists making informed choices leveraging full potential available more precise outcomes.
Язык: Английский
Процитировано
0
Communications in computer and information science, Год журнала: 2023, Номер unknown, С. 213 - 227
Опубликована: Ноя. 29, 2023
Язык: Английский
Процитировано
0