Product Progression: a machine learning approach to forecasting industrial upgrading

• URL: http://arxiv.org/abs/2105.15018v1
• Date: Mon, 31 May 2021 14:59:37 GMT
• Title: Product Progression: a machine learning approach to forecasting industrial upgrading
• Authors: Giambattista Albora, Luciano Pietronero, Andrea Tacchella, Andrea Zaccaria
• Abstract summary: We find that the key object to forecast is the activation of new products, and that tree-based algorithms clearly overperform both the quite strong auto-correlation benchmark and the other supervised algorithms. Our approach has direct policy implications, providing a quantitative and scientifically tested measure of the feasibility of introducing a new product in a given country.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Economic complexity methods, and in particular relatedness measures, lack a systematic evaluation and comparison framework. We argue that out-of-sample forecast exercises should play this role, and we compare various machine learning models to set the prediction benchmark. We find that the key object to forecast is the activation of new products, and that tree-based algorithms clearly overperform both the quite strong auto-correlation benchmark and the other supervised algorithms. Interestingly, we find that the best results are obtained in a cross-validation setting, when data about the predicted country was excluded from the training set. Our approach has direct policy implications, providing a quantitative and scientifically tested measure of the feasibility of introducing a new product in a given country.

Related papers

• Predictions as Surrogates: Revisiting Surrogate Outcomes in the Age of AI [12.569286058146343]
  We establish a formal connection between the decades-old surrogate outcome model in biostatistics and the emerging field of prediction-powered inference (PPI) We develop recalibrated prediction-powered inference, a more efficient approach to statistical inference than existing PPI proposals. We demonstrate significant gains in effective sample size over existing PPI proposals via three applications leveraging state-of-the-art machine learning/AI models.
  arXiv  Detail & Related papers  (2025-01-16T18:30:33Z)
• Attribute-to-Delete: Machine Unlearning via Datamodel Matching [65.13151619119782]
  Machine unlearning -- efficiently removing a small "forget set" training data on a pre-divertrained machine learning model -- has recently attracted interest. Recent research shows that machine unlearning techniques do not hold up in such a challenging setting.
  arXiv  Detail & Related papers  (2024-10-30T17:20:10Z)
• Ranking and Combining Latent Structured Predictive Scores without Labeled Data [2.5064967708371553]
  This paper introduces a novel structured unsupervised ensemble learning model (SUEL) It exploits the dependency between a set of predictors with continuous predictive scores, rank the predictors without labeled data and combine them to an ensembled score with weights. The efficacy of the proposed methods is rigorously assessed through both simulation studies and real-world application of risk genes discovery.
  arXiv  Detail & Related papers  (2024-08-14T20:14:42Z)
• Prediction of rare events in the operation of household equipment using co-evolving time series [1.1249583407496218]
  Our approach involves a weighted autologistic regression model, where we leverage the temporal behavior of the data to enhance predictive capabilities. Evaluation on synthetic and real-world datasets confirms that our approach outperform state-of-the-art of predicting home equipment failure methods.
  arXiv  Detail & Related papers  (2023-12-15T00:21:00Z)
• A review of predictive uncertainty estimation with machine learning [0.0]
  We review the topic of predictive uncertainty estimation with machine learning algorithms. We discuss the related metrics (consistent scoring functions and proper scoring rules) for assessing probabilistic predictions. The review expedites our understanding on how to develop new algorithms tailored to users' needs.
  arXiv  Detail & Related papers  (2022-09-17T10:36:30Z)
• Non-Clairvoyant Scheduling with Predictions Revisited [77.86290991564829]
  In non-clairvoyant scheduling, the task is to find an online strategy for scheduling jobs with a priori unknown processing requirements. We revisit this well-studied problem in a recently popular learning-augmented setting that integrates (untrusted) predictions in algorithm design. We show that these predictions have desired properties, admit a natural error measure as well as algorithms with strong performance guarantees.
  arXiv  Detail & Related papers  (2022-02-21T13:18:11Z)
• Learning Predictions for Algorithms with Predictions [49.341241064279714]
  We introduce a general design approach for algorithms that learn predictors. We apply techniques from online learning to learn against adversarial instances, tune robustness-consistency trade-offs, and obtain new statistical guarantees. We demonstrate the effectiveness of our approach at deriving learning algorithms by analyzing methods for bipartite matching, page migration, ski-rental, and job scheduling.
  arXiv  Detail & Related papers  (2022-02-18T17:25:43Z)
• Test-time Collective Prediction [73.74982509510961]
  Multiple parties in machine learning want to jointly make predictions on future test points. Agents wish to benefit from the collective expertise of the full set of agents, but may not be willing to release their data or model parameters. We explore a decentralized mechanism to make collective predictions at test time, leveraging each agent's pre-trained model.
  arXiv  Detail & Related papers  (2021-06-22T18:29:58Z)
• Double Robust Representation Learning for Counterfactual Prediction [68.78210173955001]
  We propose a novel scalable method to learn double-robust representations for counterfactual predictions. We make robust and efficient counterfactual predictions for both individual and average treatment effects. The algorithm shows competitive performance with the state-of-the-art on real world and synthetic data.
  arXiv  Detail & Related papers  (2020-10-15T16:39:26Z)
• Combining Task Predictors via Enhancing Joint Predictability [53.46348489300652]
  We present a new predictor combination algorithm that improves the target by i) measuring the relevance of references based on their capabilities in predicting the target, and ii) strengthening such estimated relevance. Our algorithm jointly assesses the relevance of all references by adopting a Bayesian framework. Based on experiments on seven real-world datasets from visual attribute ranking and multi-class classification scenarios, we demonstrate that our algorithm offers a significant performance gain and broadens the application range of existing predictor combination approaches.
  arXiv  Detail & Related papers  (2020-07-15T21:58:39Z)
• Profit-oriented sales forecasting: a comparison of forecasting techniques from a business perspective [3.613072342189595]
  This paper compares a large array of techniques for 35 times series that consist of both industry data from the Coca-Cola Company and publicly available datasets. It introduces a novel and completely automated profit-driven approach that takes into account the expected profit that a technique can create during both the model building and evaluation process.
  arXiv  Detail & Related papers  (2020-02-03T14:50:24Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.