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
- 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) - Supervised learning from noisy observations: Combining machine-learning
techniques with data assimilation [0.6091702876917281]
We show how to optimally combine forecast models and their inherent uncertainty with incoming noisy observations.
We show that the obtained forecast model has remarkably good forecast skill while being computationally cheap once trained.
Going beyond the task of forecasting, we show that our method can be used to generate reliable ensembles for probabilistic forecasting as well as to learn effective model closure in multi-scale systems.
arXiv Detail & Related papers (2020-07-14T22:29:37Z) - SAMBA: Safe Model-Based & Active Reinforcement Learning [59.01424351231993]
SAMBA is a framework for safe reinforcement learning that combines aspects from probabilistic modelling, information theory, and statistics.
We evaluate our algorithm on a variety of safe dynamical system benchmarks involving both low and high-dimensional state representations.
We provide intuition as to the effectiveness of the framework by a detailed analysis of our active metrics and safety constraints.
arXiv Detail & Related papers (2020-06-12T10:40:46Z) - Introduction to Rare-Event Predictive Modeling for Inferential
Statisticians -- A Hands-On Application in the Prediction of Breakthrough
Patents [0.0]
We introduce a machine learning (ML) approach to quantitative analysis geared towards optimizing the predictive performance.
We discuss the potential synergies between the two fields against the backdrop of this, at first glance, target-incompatibility.
We are providing a hands-on predictive modeling introduction for a quantitative social science audience while aiming at demystifying computer science jargon.
arXiv Detail & Related papers (2020-03-30T13:06:25Z) - 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.