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on Forecasting |
| By: | Opeyemi Sheu Alamu; Md Kamrul Siam |
| Abstract: | A comparative analysis of deep learning models and traditional statistical methods for stock price prediction uses data from the Nigerian stock exchange. Historical data, including daily prices and trading volumes, are employed to implement models such as Long Short Term Memory (LSTM) networks, Gated Recurrent Units (GRUs), Autoregressive Integrated Moving Average (ARIMA), and Autoregressive Moving Average (ARMA). These models are assessed over three-time horizons: short-term (1 year), medium-term (2.5 years), and long-term (5 years), with performance measured by Mean Squared Error (MSE) and Mean Absolute Error (MAE). The stability of the time series is tested using the Augmented Dickey-Fuller (ADF) test. Results reveal that deep learning models, particularly LSTM, outperform traditional methods by capturing complex, nonlinear patterns in the data, resulting in more accurate predictions. However, these models require greater computational resources and offer less interpretability than traditional approaches. The findings highlight the potential of deep learning for improving financial forecasting and investment strategies. Future research could incorporate external factors such as social media sentiment and economic indicators, refine model architectures, and explore real-time applications to enhance prediction accuracy and scalability. |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2410.07220 |
| By: | Te Li; Mengze Zhang; Yan Zhou |
| Abstract: | Against the backdrop of increasingly severe global environmental changes, accurately predicting and meeting renewable energy demands has become a key challenge for sustainable business development. Traditional energy demand forecasting methods often struggle with complex data processing and low prediction accuracy. To address these issues, this paper introduces a novel approach that combines deep learning techniques with environmental decision support systems. The model integrates advanced deep learning techniques, including LSTM and Transformer, and PSO algorithm for parameter optimization, significantly enhancing predictive performance and practical applicability. Results show that our model achieves substantial improvements across various metrics, including a 30% reduction in MAE, a 20% decrease in MAPE, a 25% drop in RMSE, and a 35% decline in MSE. These results validate the model's effectiveness and reliability in renewable energy demand forecasting. This research provides valuable insights for applying deep learning in environmental decision support systems. |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2410.15286 |
| By: | Inna S. Lola (National Research University Higher School of Economics); Dmitry Asoskov (National Research University Higher School of Economics) |
| Abstract: | This paper investigates the utility of business uncertainty indicators as predictive tools for forecasting economic activity in the context of Russia. In an era characterized by global economic volatility and geopolitical shifts, understanding the dynamics of economic uncertainty and its impact on overall economic performance is of paramount importance. The study utilizes a comprehensive dataset based on the results of business tendency surveys in Russia, spanning the period from 2009 to the first half of 2024. Given the importance of uncertainty in shaping economic outcomes, the central research question of this study is: “Can uncertainty indicators predict business activity in Russia or not?”. To address this question, we compared two alternative approaches to calculating business uncertainty: the ex-ante approach, which uses the business community's assessments of future business trends to measure uncertainty as a measure of the dispersion of opinions expressed, and the ex-post approach, which uses entrepreneurial assessments of both future and current trends to determine business uncertainty as the degree of deviation of entrepreneurial expectations from the real picture. National indicators and sectoral indicators were calculated for the mining and quarrying industry, manufacturing industry, construction, retail trade, wholesale trade and services. For most of the industries under consideration (except for the construction and service sector) and at the national level, the specifications of vector autoregression models that were effective for forecasting real indicators of economic activity, characterized by lower forecast errors compared to standard autoregressive models, were built. According to the results obtained, at the national level, when forecasting GDP, clear preference should be given to the ex-post indicator. |
| Keywords: | uncertainty, business tendency survey, Russia, forecasting. |
| JEL: | C82 D81 E32 |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:hig:wpaper:128sti2024 |