arbiter
JournalsPricingAccount

The Long Short-Term Memory Algorithm and the Autoregressive Integrated Moving Average Approach in Business Tendency Survey

Ewa Ratuszny

Central European Journal of Economic Modelling and Econometrics2025https://doi.org/10.24425/cejeme.2025.155562article
ABDC C
Weight
0.50

What the paper says

This study investigates the effectiveness of machine learning models in forecasting construction indicators derived from Business Tendency Survey data. Specifically, we compare the performance of traditional statistical models such as the autoregressive integrated moving average (ARIMA) with long short-term memory (LSTM) networks and hybrid approaches combining both. Using a range of economic variables -- including sector and economic evaluations, production, financial situation, investments, and sentiment indicator (IRGBUD) -- we evaluate model accuracy across testing dataset and rolling forecast strategy to assess consistency over time. Results demonstrate that while LSTM networks capture non-linear dependencies and temporal patterns, ARIMA-based models consistently outperforms LSTM in scenarios involving seasonal and cyclical structures. The findings highlight that the choice of model should align with the nature of the time series, particularly in relation to seasonality, volatility, and trend dynamics. This work offers practical implications for improving economic forecasting with machine learning in survey-based environments.

Open paper page →

Cite this paper

https://doi.org/https://doi.org/10.24425/cejeme.2025.155562

Or copy a formatted citation

@article{ewa2025,
  title        = {{The Long Short-Term Memory Algorithm and the Autoregressive Integrated Moving Average Approach in Business Tendency Survey}},
  author       = {Ewa Ratuszny},
  journal      = {Central European Journal of Economic Modelling and Econometrics},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.24425/cejeme.2025.155562},
}

Paste directly into BibTeX, Zotero, or your reference manager.

Flag this paper

The Long Short-Term Memory Algorithm and the Autoregressive Integrated Moving Average Approach in Business Tendency Survey

Flags are reviewed by the Arbiter methodology team within 5 business days.

Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.