Forecasting with Business and Consumer Survey Data

Clavería, Oscar

doi:10.3390/forecast3010008

Cited by 3 publications

(2 citation statements)

References 94 publications

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“…In recent decades, many researchers and practitioners have tried to predict stock prices using various methods, including time-series-based prediction methods [7,8], machine learningbased prediction methods [9,10], deep learning-based prediction methods [11][12][13][14], and so on. However, due to the characteristics of stock prices, such as non-linearity, high noise, and variability, it is often difficult to achieve the desired prediction results with these methods [15][16][17][18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

Time-Series Neural Network: A High-Accuracy Time-Series Forecasting Method Based on Kernel Filter and Time Attention

Zhang,

Wang,

et al. 2023

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This research introduces a novel high-accuracy time-series forecasting method, namely the Time Neural Network (TNN), which is based on a kernel filter and time attention mechanism. Taking into account the complex characteristics of time-series data, such as non-linearity, high dimensionality, and long-term dependence, the TNN model is designed and implemented. The key innovations of the TNN model lie in the incorporation of the time attention mechanism and kernel filter, allowing the model to allocate different weights to features at each time point, and extract high-level features from the time-series data, thereby improving the model’s predictive accuracy. Additionally, an adaptive weight generator is integrated into the model, enabling the model to automatically adjust weights based on input features. Mainstream time-series forecasting models such as Recurrent Neural Networks (RNNs) and Long Short-Term Memory Networks (LSTM) are employed as baseline models and comprehensive comparative experiments are conducted. The results indicate that the TNN model significantly outperforms the baseline models in both long-term and short-term prediction tasks. Specifically, the RMSE, MAE, and R2 reach 0.05, 0.23, and 0.95, respectively. Remarkably, even for complex time-series data that contain a large amount of noise, the TNN model still maintains a high prediction accuracy.

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Section: Introductionmentioning

confidence: 99%

Time-Series Neural Network: A High-Accuracy Time-Series Forecasting Method Based on Kernel Filter and Time Attention

Zhang,

Wang,

et al. 2023

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“…In a related study, Claveria mentioned that in the context of increasing uncertainty, the perceptions of companies and consumers on the expected development of the main variables affecting their activities are crucial for economic forecasting [3]. Work focused on using forecast content from surveys of economic trends is highlighted, evaluating the forecast performance of quarterly unemployment expectations in the euro area obtained through machine learning methods.…”

Section: Introductionmentioning

confidence: 99%

Psychological Prediction of Digital Economy Consumption Considering Deep Neural Network

2022

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Scientific and accurate forecasts can not only help people to formulate correct work plans and action arrangements in a timely manner, but also provide important policy recommendations for economic development and promote stable and healthy economic development. In real life, everyone hopes to obtain very effective forecast results and provide necessary solutions for scientifically responding to future market changes. The main purpose of this paper is to analyze and study the prediction of consumer psychology in the digital economy based on DNNs. This paper establishes an analytical framework for the dynamic correlation characteristics of consumption and economic growth, and innovatively comprehensively evaluates the changes of the correlation between the two over time from three aspects: gravity coupling, cycle synergy and interactive influence. To compare the forecast results, the error function was used to calculate the error of the three forecast methods using the mean squared error. The experimental study shows that the DNN algorithm in this paper has the best prediction effect through the comparison charts and error comparison tables of three prediction results. The prediction error of the multiple linear regression algorithm is 0.483, the prediction error of the principal component regression algorithm is 0.214, and the prediction error of the DNN algorithm is 0.059, which is an order of magnitude higher than the previous two algorithms in accuracy.

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Potential of business uncertainty indicators in forecasting economic activity: The case of Russia

Lola,

Asoskov

2024

RUJEC

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This study 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 the dispersion of opinions expressed, and the ex‑post approach, which applies 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.

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Forecasting with Business and Consumer Survey Data

Cited by 3 publications

References 94 publications

Time-Series Neural Network: A High-Accuracy Time-Series Forecasting Method Based on Kernel Filter and Time Attention

Time-Series Neural Network: A High-Accuracy Time-Series Forecasting Method Based on Kernel Filter and Time Attention

Psychological Prediction of Digital Economy Consumption Considering Deep Neural Network

Potential of business uncertainty indicators in forecasting economic activity: The case of Russia

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