Improved sales forecasting for new products using transfer learning.
problem Insufficient training data for new products leads to inaccurate sales forecasts.
method Network-based Transfer Learning approach for deep neural networks.
result Deep neural networks' prediction accuracy for food sales forecasting can be effectively increased.
Models assess how USDA orange production forecasts impact FCOJ market decisions.
problem High volatility in FCOJ futures due to limited U.S. orange production.
method Developed models to assess the impact of USDA October orange production forecasts on FCOJ market participants.
result Probabilistic forecasts of USDA production forecast error can reduce FCOJ price volatility.
Improved forecast accuracy for Knitwear by 20% using adaptive AI/ML model.
problem Low accuracy in demand forecasts for Knitwear product category.
method Dynamic selection of the best algorithm from an algorithm rack based on performance and context.
result Increased forecast accuracy from 60% to 80% for Knitwear.
The paper proposes a machine learning approach for production forecasting without model calibration.
problem Generating accurate production forecasts for reservoir development.
method Sequential model aggregation using machine learning algorithms without model calibration.
result The proposed method provides robust multi-step-ahead production forecasts.
CDLF predicts product life-cycles in cold-start phases with high accuracy.
problem Forecasting new products in early phases when data is scarce.
method Conditional Diffusion Life-cycle Forecaster (CDLF) combining static descriptors, reference trajectories, and new observations.
result CDLF outperforms classical models in accuracy and probabilistic forecasting.
A new method forecasts hourly electricity prices considering product dynamics and limit order book signals.
problem High volatility and imbalance in power systems due to renewable energy and flexible demand.
method Incorporates short-term features from hourly and quarter-hourly products, including limit order book and neighboring product signals.
result Features from the limit order book are most influential, and neighboring product signals improve forecast accuracy.
E-commerce sales forecast using LSTM with cross-series information.
problem Accurate sales forecasting in e-commerce with limited univariate methods.
method Global training of LSTM on product assortment hierarchy, incorporating cross-series information.
result LSTM achieves competitive results on Walmart.com dataset, outperforming state-of-the-art techniques.
New method improves sales forecasting accuracy using tensor factorization.
problem Improving sales forecasting accuracy in retail businesses.
method Advanced Temporal Latent-factor Approach to Sales forecasting (ATLAS) using tensor factorization.
result Accurate and individualized prediction for sales across multiple stores and products.
A new model predicts fashion demand 6-12 months ahead, boosting retailer profits.
problem Accurate demand forecasting for fashion retailers with short product life cycles.
method Product age-based forecast model, incorporating unique feature engineering.
result Significant revenue uplift of 41% compared to retailer's plan.
Proposes a network framework for forecasting futures with different expirations.
problem Forecasting E-mini S\&P 500 and CBOE Volatility Index futures with different expirations.
method A novel data-driven network framework using GCN-LSTM, visualizing correlation structures, and enhancing LSTM's predictive power.
result Enhanced predictive power of future forecasts through a multi-channel Graph Convolutional Network.
THieF improves day-ahead electricity price prediction accuracy by reconciling hourly and block forecasts.
problem Improving accuracy in predicting day-ahead electricity prices.
method Temporal hierarchy forecasting (THieF) reconciling hourly and block forecasts.
result THieF significantly improves accuracy (up to 13%) at all levels of prediction.
Paper evaluates how forecast errors affect optimal utilisation in production planning.
problem Forecast errors impact optimal utilisation in production planning.
method Simulation and mixed integer programming for stochastic demand.
result Forecast errors significantly affect optimal costs in production planning.
New data set and method for BTO supply chain demand forecasting.
problem Lack of demand forecasting methods for BTO supply chains.
method Proposes a novel data transformation technique for BTO products.
result Approach compares well to state-of-the-art methods and is easy to implement.
The study improves load forecasting for electricity consumers using advanced machine learning models.
problem Improving short-term load forecasting for effective scheduling and decision-making.
method Proposes and evaluates statistical nonlinear models, including LSTM and GRU, for 15-min frequency electricity load forecasting.
result Advanced models outperform other models in out-of-sample forecasting accuracy, as shown by the Diebold-Mariano test.
Deep learning model forecasts PV power production with high accuracy.
problem Accurate forecasting of photovoltaic power production is needed for electricity infrastructure.
method Sequence to sequence model with attention mechanism using weather predictions and historical data.
result The model outperforms existing methods in forecast skill score.
Robust forecast framework reduces distribution error by 63%.
problem Accurate distribution forecast for planning decisions.
method Backtest-based bootstrap and adaptive residual selection.
result Reduces Absolute Coverage Error by more than 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.
problem Volatility and nonlinearity in OEE time series data.
method Topological Data Analysis (TDA) to transform raw data into structured knowledge.
result Improves OEE forecasting accuracy by at least 17%.
Retail company uses Prophet algorithm for accurate sales forecasting.
problem Accurate sales forecasting in the retail industry.
method Facebook's Prophet algorithm and backtesting strategy.
result Framework demonstrates real-world use case capabilities.
LSTMs improve demand forecasting for e-grocery products.
problem Improving demand forecasting accuracy in e-grocery retail.
method Developed and tested univariate and multivariate LSTM models for 100 fast-moving consumer goods.
result LSTMs outperform traditional models for food products, especially in beverage category.
Deep learning improves weather modeling for electricity load forecasting.
problem Accurate load and renewable energy forecasting requires complex spatio-temporal weather modeling.
method Automated spatio-temporal feature extraction using deep neural networks.
result Deep learning outperforms traditional methods in French national load forecasting.
A new multi-phase approach improves supply chain forecasting accuracy.
problem Improving forecast accuracy for hierarchical supply chain demands.
method Independent child-level forecasting followed by parent-level estimation.
result 82-90% improvement in forecast accuracy compared to traditional methods.
Study identifies regions where scoring rules reliably detect forecast errors.
problem Insufficient reliability of scoring rules in evaluating multivariate probabilistic forecasts.
method Systematic finite-sample analysis of proper scoring rules on synthetic and real-world data.
result Identified regions of reliability for scoring rules in time-series forecasting.
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…
Improved financial forecasts with deep neural networks and curriculum learning.
problem Scaling traditional forecasting models for high-granularity financial data.
method Applied Encoder-Decoder LSTMs and Dilated CNNs from NLP and CV, with curriculum learning.
result Approximately 30% improvement in forecast accuracy compared to traditional methods.
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.
problem Cold-start PV forecasting
method Zero-shot pipeline with synthetic histories
result TabPFN-TS achieves the lowest error under Real Feedback strategy
Study builds dataset and benchmarks ML models for accurate solar and wind power forecasting in France.
problem Accurate prediction of non-dispatchable renewable energy sources for grid stability and price prediction.
method Comprehensive methodology using machine learning models trained with spatially explicit weather data and production site capacity.
result Neural networks outperform traditional models in forecasting solar and wind power production in France.
SPADE improves demand forecasting accuracy by 4.5% for post-promotion periods.
problem Overreacting to peak events in demand forecasting leads to biased forecasts.
method SPADE splits forecasting into two tasks: one for peak events and another for post-peak events, using masked convolution filters and a specialized Peak Attention module.
result Overall PPE improvement of 4.5%, 30% improvement for most affected forecasts after promotions and holidays, and 3.9% improvement in PE accuracy.
A new metric optimizes forecasts for lumpy, intermittent demand.
problem Inaccurate demand forecasts lead to suboptimal logistics and production.
method Developed a novel metric that considers both statistical and business aspects.
result The new metric yields more accurate predictions for lumpy and intermittent demand.
Gradient boosting predicts promotion efficiency using multiple performance indicators.
problem Forecasting promotion efficiency in FMCG retail.
method Gradient boosting applied to six performance indicators for different product groups.
result Models accurately forecast promotion efficiency, optimizing marketing strategies.
Copulas model cross-product effects in intraday power markets.
problem Intraday power markets' cross-product effects are not adequately addressed by existing univariate approaches.
method Copulas and latent beta regression for modeling high-dimensional intraday price return vector, with time-varying dependence parameter.
result Modeling cross-product effects improves forecasting performance.
The study finds that low frequency macroeconomic variables are more important for short-term electricity price forecasting.
problem Improving short-term forecasting of daily electricity prices using macroeconomic variables.
method Developed a Bayesian reverse unrestricted MIDAS model to account for frequency mismatch.
result Inclusion of macroeconomic low frequency variables improves short-term forecasts more than using only surveys or industrial production data.
The paper explores features from orderbooks to improve intraday electricity price forecasting.
problem Improving probabilistic forecasting of intraday electricity prices.
method Extracted 384 features from orderbooks, selected powerful features, and benchmarked models across two countries and product types.
result Revealed an asymmetric generalization phenomenon in electricity price forecasting models.
Model equilibrium price in intraday electricity markets with uncertainty.
problem Formulate equilibrium model for intraday electricity trading with balancing constraints and uncertainty.
method Develop equilibrium model with agents' balancing constraints, forecasted consumption, production uncertainties, and Markov chain outages.
result Existence and uniqueness of equilibrium price as a martingale, with insights into price formation and impact of uncertainty.
A new method for analyzing product competition using low-dimensional embeddings.
problem Computational challenges in studying product-level competition for millions of products.
method Product2Vec, a method based on representation learning algorithm Word2Vec.
result The method produces more accurate demand forecasts and price elasticities compared to state-of-the-art models.
Dynamic probabilistic forecasts guide optimal decisions in uncertain processes.
problem Optimal decision making in processes influenced by uncertain random factors.
method Stochastic models for probabilistic forecast evolution, calibrated from ensemble forecasts.
result Optimal decision strategies determined using dynamic probabilistic forecasts.
Deep learning improves PV generation quantile forecasting.
problem Accurate probabilistic forecasting of PV generation.
method Developed an encoder-decoder deep learning model for multi-output quantile PV forecasting.
result The model improves forecast quality and computational efficiency.
RNN models improve demand forecasting for diverse products.
problem Accurately predicting purchase patterns of popular products with sparse and heterogeneous data.
method Survival analysis with Recurrent Neural Networks (RNN) to model inter-arrival times and partially observed data.
result RNN-based approach achieves substantial improvements over traditional methods.
HierarchicalForecast provides a Python framework for coherent hierarchical forecasting.
problem Ensuring forecasts at disaggregate levels add up to aggregate forecasts.
method Preprocessed datasets, evaluation metrics, and statistical baseline models.
result Python-based reference framework for statistical and ML forecasting.
We present a new Monte-Carlo methodology to forecast the crude oil production of Norway and the U.K. based on a two-step process, (i) the nonlinear extrapolation of the current/past performances of individual oil fields and (ii) a stochastic model of the frequency of future oil field discoveries. Compared with the stan…
New model predicts sales of new products with short life cycles.
problem Forecasting sales of new products with short lead times and life cycles.
method Developed an exponential factorization machine (EFM) to consider attributes and pairwise interactions.
result EFM model outperforms existing models in terms of MAPE and MAE.
New method for disaggregate electricity demand forecasting at household level.
problem Challenges in forecasting electricity demand at individual household level.
method Additive stacking method for probabilistic disaggregate electricity demand forecasting.
result Improved accuracy in disaggregate electricity demand forecasting.
The paper analyzes and forecasts intraday electricity prices using econometric models.
problem Analyzing and forecasting the efficiency of the German Intraday Continuous electricity market.
method Multivariate econometric time series model with lasso and elastic net techniques.
result The model provides new insights into the ID3-Price behavior and market efficiency. Generative AI boosts analyst reports but increases forecast errors.
problem Improving financial analyst reports with AI.
method Natural experiment using FactSet's AI platform.
result AI-assisted reports are more comprehensive but lead to higher forecast errors.
The growing conflicts in and about oil exporting regions and speculations about volatile oil prices during the last decade have renewed the public interest in predictions for the near future oil production and consumption. Unfortunately, studies from only 10 years ago, which tried to forecast the oil production during …
Enhances neural forecasting for hierarchically organized time series data.
problem Probabilistic coherent forecasting of time series data across different levels of aggregation.
method Proposes a coherent multivariate mixture output for neural forecasting architectures, optimizing with a composite likelihood objective.
result 13.2% average accuracy improvements on most datasets compared to state-of-the-art baselines.
Forecasting technological progress is of great interest to engineers, policy makers, and private investors. Several models have been proposed for predicting technological improvement, but how well do these models perform? An early hypothesis made by Theodore Wright in 1936 is that cost decreases as a power law of cumul…