# Forecasting Techniques and Applications

**Type:** Topics  
**Canonical URL:** https://scholariq.org/topics/forecasting-techniques-and-applications/

## Facts

| Field | Value |
| --- | --- |
| Description | This cluster of papers focuses on advances in time series forecasting methods, including topics such as exponential smoothing, machine learning techniques, expert judgment, inventory management, demand forecasting, neural networks, and forecast combination. |
| Domain | Social Sciences |
| Field | Decision Sciences |
| OpenAlex ID | t11918 |
| Works | 49 |

## Topic papers all

Showing 15 of 49.

- [Regression modeling strategies : with applications to linear models, logistic and ordinal regression, and survival analysis](https://scholariq.org/papers/regression-modeling-strategies-with-applications-to-linear-models-logistic-and/)
- [Decisions from Experience and the Effect of Rare Events in Risky Choice](https://scholariq.org/papers/decisions-from-experience-and-the-effect-of-rare-events-in-risky-choice/)
- [Uncertain Judgements: Eliciting Experts' Probabilities](https://scholariq.org/papers/uncertain-judgements-eliciting-experts-probabilities/)
- [Predicting stock and stock price index movement using Trend Deterministic Data Preparation and machine learning techniques](https://scholariq.org/papers/predicting-stock-and-stock-price-index-movement-using-trend-deterministic-data/)
- [Applications of deep learning in stock market prediction: Recent progress](https://scholariq.org/papers/applications-of-deep-learning-in-stock-market-prediction-recent-progress/)
- [Forecasting: Methods and Applications](https://scholariq.org/papers/forecasting-methods-and-applications/)
- [Predicting stock market index using fusion of machine learning techniques](https://scholariq.org/papers/predicting-stock-market-index-using-fusion-of-machine-learning-techniques/)
- [META‐ANALYSIS OF ECONOMICS RESEARCH REPORTING GUIDELINES](https://scholariq.org/papers/meta-analysis-of-economics-research-reporting-guidelines/)
- [Time Series Forecasting with Neural Networks: A Comparative Study Using the Air Line Data](https://scholariq.org/papers/time-series-forecasting-with-neural-networks-a-comparative-study-using-the-air/)
- [Reducing Overconfidence in the Interval Judgments of Experts](https://scholariq.org/papers/reducing-overconfidence-in-the-interval-judgments-of-experts/)
- [A choice prediction competition: Choices from experience and from description](https://scholariq.org/papers/a-choice-prediction-competition-choices-from-experience-and-from-description/)
- [Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States](https://scholariq.org/papers/evaluation-of-individual-and-ensemble-probabilistic-forecasts-of-covid-19/)
- [Expert Status and Performance](https://scholariq.org/papers/expert-status-and-performance/)
- [The Wisdom of Many in One Mind](https://scholariq.org/papers/the-wisdom-of-many-in-one-mind/)
- [Exploring the use of deep neural networks for sales forecasting in fashion retail](https://scholariq.org/papers/exploring-the-use-of-deep-neural-networks-for-sales-forecasting-in-fashion/)

## Topic primary papers

- [Regression modeling strategies : with applications to linear models, logistic and ordinal regression, and survival analysis](https://scholariq.org/papers/regression-modeling-strategies-with-applications-to-linear-models-logistic-and/)
- [Uncertain Judgements: Eliciting Experts' Probabilities](https://scholariq.org/papers/uncertain-judgements-eliciting-experts-probabilities/)
- [Forecasting: Methods and Applications](https://scholariq.org/papers/forecasting-methods-and-applications/)
- [Time Series Forecasting with Neural Networks: A Comparative Study Using the Air Line Data](https://scholariq.org/papers/time-series-forecasting-with-neural-networks-a-comparative-study-using-the-air/)
- [Exploring the use of deep neural networks for sales forecasting in fashion retail](https://scholariq.org/papers/exploring-the-use-of-deep-neural-networks-for-sales-forecasting-in-fashion/)
- [Identifying and Cultivating Superforecasters as a Method of Improving Probabilistic Predictions](https://scholariq.org/papers/identifying-and-cultivating-superforecasters-as-a-method-of-improving/)
- [Redefine statistical significance](https://scholariq.org/papers/redefine-statistical-significance-2/)
- [Predictive modeling of consumer purchase behavior on social media: Integrating theory of planned behavior and machine learning for actionable insights](https://scholariq.org/papers/predictive-modeling-of-consumer-purchase-behavior-on-social-media-integrating/)
- [Predicting the reverse flow of spare parts in a complex supply chain: contribution of hybrid machine learning methods in an industrial context](https://scholariq.org/papers/predicting-the-reverse-flow-of-spare-parts-in-a-complex-supply-chain-2/)
- [Can crowdsourcing improve prediction accuracy in fashion retail buying?](https://scholariq.org/papers/can-crowdsourcing-improve-prediction-accuracy-in-fashion-retail-buying/)
- [Integration approaches of forecasting methods selection with inventory management indicators in the case of spare parts supply chain](https://scholariq.org/papers/integration-approaches-of-forecasting-methods-selection-with-inventory/)
- [Predicting the reverse flow of spare parts in a complex supply chain: contribution of hybrid machine learning methods in an industrial context](https://scholariq.org/papers/predicting-the-reverse-flow-of-spare-parts-in-a-complex-supply-chain/)
- [The value of crowdsourcing in apparel and fashion retail buying](https://scholariq.org/papers/the-value-of-crowdsourcing-in-apparel-and-fashion-retail-buying/)
- [An Attention-Driven Hybrid CNN-LSTM Model for Accurate Prediction in Sustainable Supply Chain Management](https://scholariq.org/papers/an-attention-driven-hybrid-cnn-lstm-model-for-accurate-prediction-in-sustainable/)
- [Data-Driven Optimization of Spare Parts Maintenance in Closed-Loop Supply Chains](https://scholariq.org/papers/data-driven-optimization-of-spare-parts-maintenance-in-closed-loop-supply-chains/)

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Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.