Including news data in forecasting macro economic performance of China

Zhu

Mathematical Problems in Engineering

et al. 2022

Macroeconomic situation is the overall performance of the economic situation of a country and region. Making accurate forecasts of macroeconomic trends is of great significance for analyzing the success or failure of macroeconomic control policies, evaluating the quality of economic system operation, and correctly formulating future development planning strategies. The macroeconomic system is a nonlinear system, the environment is constantly changing, and additional disturbing factors directly affect the operation of the macroeconomic system, which has a great impact on the forecast results. The historical information required for macroeconomic modeling is unstable, unclear, and incomplete, which makes it very difficult to solve such problems with traditional forecasting methods. In response to the multivariate and nonlinear characteristics of macroeconomic forecasting, this paper proposes the application of artificial neural networks for forecasting. This paper introduces the recurrent neural network into the field of economic forecasting to solve the problems of the traditional BP (back propagation) neural network method. The experimental data are verified and the experimental results prove that the studied scheme based PSO-GRU improve the performance of economic forecasting.

Section: Introductionmentioning

confidence: 99%

Regional Economic Forecasting Method Based on Recurrent Neural Network

Zhu

Mathematical Problems in Engineering

et al. 2022

“…GDP prediction. Lunde and Torkar (2020) exploit more than 120 predictors and then perform a principal component analysis (PCA) to reduce the number of variables. Despite the inclusion of different sources of information in their set-up, the PCA does not provide the economic interpretation of the results.…”

Section: Introductionmentioning

confidence: 99%

GDP Forecasting: Machine Learning, Linear or Autoregression?

Maccarrone

Morelli

Spadaccini

2021

Front. Artif. Intell.

This paper compares the predictive power of different models to forecast the real U.S. GDP. Using quarterly data from 1976 to 2020, we find that the machine learning K-Nearest Neighbour (KNN) model captures the self-predictive ability of the U.S. GDP and performs better than traditional time series analysis. We explore the inclusion of predictors such as the yield curve, its latent factors, and a set of macroeconomic variables in order to increase the level of forecasting accuracy. The predictions result to be improved only when considering long forecast horizons. The use of machine learning algorithm provides additional guidance for data-driven decision making.

A technique to forecast Pakistan’s news using deep hybrid learning model

Ihsan,

Khurshid,

Shoaib

et al. 2024

Int. j. inf. tecnol.

Forecasting future events is a challenging task that can have a signi cant impact on decision-making and policy-making. In this research, we focus on forecasting news related to Pakistan. Despite the importance of accurate predictions in this eld, there currently exists no dataset for forecasting Pakistani news, speci cally with regard to politics. In order to address this gap, our research aims to create the rst Pakistani news dataset for forecasting Pakistan news that is mostly related to the politics of Pakistan. This dataset was collected from various sources, including Pakistani news websites and social media platforms, as well as frequently asked questions about Pakistani politics. We develop a forecasting model using this dataset and evaluate the effectiveness of cutting-edge deep hybrid learning techniques incorporating neural networks, random forest, Word2vec, NLP, and Naive Bayes. To the best of our understanding, no research has been done on the application of a deep hybrid learning model-a blend of deep learning and machine learning-for news forecasting. The accuracy of the forecasting model is 97%. According to our ndings, the model's performance is adequate when compared to that of other forecasting models. Our research not only lls the gap in the current literature but also presents a new challenge for large language models and has the potential to bring signi cant practical advantages in the eld of forecasting.