Regional Economy Using Hybrid Sequence‐to‐Sequence‐Based Deep Learning Approach

Abstract

In recent times, the role of the regional economy changed significantly under certain conditions of globalization and structural adjustment. The process of changing must be crucial to analyse regional economy and develop the planning of regional economy. Developing economies depend often on industries and country policies. Modern studies tend to participate in important factors in this field such as energy intensity, labour skills, local industries, resources, and local expertise. Furthermore, in this study, to start developing the regional economy and make the revolution in this field to connect it with new technology, we train the deep learning algorithm of gathering factors to manage them perfectly and make a good prediction for the future economy. Hybrid sequence to sequence (seq2seq) algorithms of deep learning fed with previous information from past years and run the system to compare the predicted result data with current information to evaluate the method to be certified for the coming years.

Regional Economic Development in the AI Era: Methods, Opportunities, and Challenges

“…DL models, such as CNN and RNN, can deal with high-dimensional and sequential data, making them particularly useful for time-series economic data analysis [17,20]. Hybrid models that combine different ML and DL methods have also been developed for more sophisticated economic data analysis [30].…”

“…AI models can learn from past data to predict future trends and changes in regional economies. For instance, deep learning-based sequence-to-sequence models have been used for regional economic prediction [30], and reinforcement learning frameworks have been employed for regional GDP prediction [10]. These predictive models assist in forecasting economic conditions, informing policy decisions, and promoting proactive economic planning and management.…”

“…Deep Learning, with its capacity to handle complex, high-dimensional data, offers even more advanced tools for predictive economic modelling. For ex-ample, Recurrent Neural Networks (RNN) and their variants like Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU), have been utilized to model time-series data in regional economies, capturing the temporal dependencies in the data to generate reliable forecasts [20,30].…”

Evaluation on the Training Effect of Applied Talents in Higher Vocational Colleges under the Background of Regional Economy

“…We can analyze regional economy from both macro and microperspectives. From the macroperspective, the regional economy can be seen as a microcosm of the economic development of the whole country, which has certain comprehensive and regional characteristics [2]. From the microlevel, the important factor a ecting the national economic development is the supply of social talents [3].…”