A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
CDLF predicts product life-cycles in cold-start phases with high accuracy.
New model predicts sales of new products with short life cycles.
A new model predicts fashion demand 6-12 months ahead, boosting retailer profits.
This paper tackles hidden technical debts in fair ML systems for Fintech.
Concurrent engineering taking into account product life-cycle factors seems to be one of the industrial challenges of the next years. Cost estimation and management are two main strategic tasks that imply the possibility of managing costs at the earliest stages of product development. This is why it is indispensable to…
An analytic model is presented that considers the evolution of a market of durable goods. The model suggests that after introduction goods spread always according to a Bass diffusion. However, this phase will be followed by a diffusion process for durable consumer goods governed by a variation-selection-reproduction me…
We provide a machine learning solution that replaces the traditional methods for deciding the pesticide application time of Sunn Pest. We correlate climate data with phases of Sunn Pest in its life-cycle and decide whether the fields should be sprayed. Our solution includes two groups of prediction models. The first gr…
As machine learning (ML) increasingly affects people and society, awareness of its potential unwanted consequences has also grown. To anticipate, prevent, and mitigate undesirable downstream consequences, it is critical that we understand when and how harm might be introduced throughout the ML life cycle. In this paper…
We use a control framework to analyze the digital vendor's profit maximization problem. The vendor captures market share by focusing costly effort on post-launch product maintenance, which influences user perception of the product and drives a revenue stream associated with product use. Our theoretical results show nec…
In this article we solve the problem of maximizing the expected utility of future consumption and terminal wealth to determine the optimal pension or life-cycle fund strategy for a cohort of pension fund investors. The setup is strongly related to a DC pension plan where additionally (individual) consumption is taken i…
In the early phases of the product life cycle, the costs controls became a major decision tool in the competitiveness of the companies due to the world competition. After defining the problems related to this control difficulties, we will present an approach using a concept of cost entity related to the design and real…
The control of the costs, as soon as possible of the product life cycle, became a major asset in the competitiveness of the companies confronted with the universalization of competition. After having proposed the problems related this control difficulties, we will present an approach defining a concept of cost entity r…
Continuous integration is an indispensable step of modern software engineering practices to systematically manage the life cycles of system development. Developing a machine learning model is no difference - it is an engineering process with a life cycle, including design, implementation, tuning, testing, and deploymen…
Improved sales forecasting for new products using transfer learning.
Models assess how USDA orange production forecasts impact FCOJ market decisions.
The determinants of the velocity of money have been examined based on life-cycle hypothesis. The velocity of money can be expressed by reciprocal of the average value of holding time which is defined as interval between participating exchanges for one unit of money. This expression indicates that the velocity is govern…
Improved forecast accuracy for Knitwear by 20% using adaptive AI/ML model.
The paper optimizes investment strategies with constraints for life-cycle models.
Modelling all possible life cycles of a company in a highly competitive economic environment gives a significant advantage to the owner in his business investment activities. This article proposes and analyses a dynamic model of a company's life cycle with known action costs and transition probabilities, that can be af…
A new method forecasts hourly electricity prices considering product dynamics and limit order book signals.
Production forecasting is a key step to design the future development of a reservoir. A classical way to generate such forecasts consists in simulating future production for numerical models representative of the reservoir. However, identifying such models can be very challenging as they need to be constrained to all a…
New method improves sales forecasting accuracy using tensor factorization.
Generating accurate and reliable sales forecasts is crucial in the E-commerce business. The current state-of-the-art techniques are typically univariate methods, which produce forecasts considering only the historical sales data of a single product. However, in a situation where large quantities of related time series …
Proposes a network framework for forecasting futures with different expirations.
THieF improves day-ahead electricity price prediction accuracy by reconciling hourly and block forecasts.
The study improves load forecasting for electricity consumers using advanced machine learning models.
The key idea of this model is that firms are the result of an evolutionary process. Based on demand and supply considerations the evolutionary model presented here derives explicitly Gibrat's law of proportionate effects as the result of the competition between products. Applying a preferential attachment mechanism for…
Deep learning model forecasts PV power production with high accuracy.
Robust forecast framework reduces distribution error by 63%.
We study the optimal trading policies for a wind energy producer who aims to sell the future production in the open forward, spot, intraday and adjustment markets, and who has access to imperfect dynamically updated forecasts of the future production. We construct a stochastic model for the forecast evolution and deter…
Study uses TDA to improve OEE forecasting in manufacturing.
Retail company uses Prophet algorithm for accurate sales forecasting.
Build-to-order (BTO) supply chains have become common-place in industries such as electronics, automotive and fashion. They enable building products based on individual requirements with a short lead time and minimum inventory and production costs. Due to their nature, they differ significantly from traditional supply …
LSTMs improve demand forecasting for e-grocery products.
A dynamic model of the product lifecycle of (nearly) homogeneous durables in polypoly markets is established. It describes the concurrent evolution of the unit sales and price of durable goods. The theory is based on the idea that the sales dynamics is determined by a meeting process of demanded with supplied product u…
Deep learning improves weather modeling for electricity load forecasting.
EBM life cycle project improves MCMC for image generation, defense, and density modeling.
Study analyzes climate-tech investments across 14 sectors.
A new multi-phase approach improves supply chain forecasting accuracy.
Study identifies regions where scoring rules reliably detect forecast errors.
Electricity production via solar energy is tackled via short-term forecasts and risk management. Our main tool is a new setting on time series. It allows the definition of "confidence bands" where the Gaussian assumption, which is not satisfied by our concrete data, may be abandoned. Those bands are quite convenient an…
In this study we model the warranty claims process and evaluate the warranty servicing costs under non-renewing and renewing free repair warranties. We assume that the repair time for rectifying the claims is non-zero and the repair cost is a function of the length of the repair time. To accommodate the ageing of the p…
Optimal scheduling of hydrogen production in dynamic pricing power market can maximize the profit of hydrogen producer; however, it highly depends on the accurate forecast of hydrogen consumption. In this paper, we propose a deep leaning based forecasting approach for predicting hydrogen consumption of fuel cell vehicl…
Cold-start PV forecasting uses synthetic histories to train time-series foundation models.
Survey of technologies for trustworthy machine learning systems.
SPADE improves demand forecasting accuracy by 4.5% for post-promotion periods.
Study builds dataset and benchmarks ML models for accurate solar and wind power forecasting in France.
For any financial organization, computing accurate quarterly forecasts for various products is one of the most critical operations. As the granularity at which forecasts are needed increases, traditional statistical time series models may not scale well. We apply deep neural networks in the forecasting domain by experi…