Tootfinder

Diffusion index forecasts under weaker loadings: PCA, ridge regression, and random projections
Tom Boot, Bart Keijsers
https://arxiv.org/abs/2506.09575 htt…

Diffusion index forecasts under weaker loadings: PCA, ridge regression, and random projections
We study the accuracy of forecasts in the diffusion index forecast model with possibly weak loadings. The default option to construct forecasts is to estimate the factors through principal component analysis (PCA) on the available predictor matrix, and use the estimated factors to forecast the outcome variable. Alternatively, we can directly relate the outcome variable to the predictors through either ridge regression or random projections. We establish that forecasts based on PCA, ridge regres…

Evaluation of Machine Learning Models in Student Academic Performance Prediction
A. G. R. Sandeepa, Sanka Mohottala
https://arxiv.org/abs/2506.08047 https:…

Evaluation of Machine Learning Models in Student Academic Performance Prediction
This research investigates the use of machine learning methods to forecast students' academic performance in a school setting. Students' data with behavioral, academic, and demographic details were used in implementations with standard classical machine learning models including multi-layer perceptron classifier (MLPC). MLPC obtained 86.46% maximum accuracy for test set across all implementations. Under 10-fold cross validation, MLPC obtained 79.58% average accuracy for test set while for train…

Whatever that Big Beautiful Bill may be, I would just like to add that our finest, most celebrated, most thoroughly understood and metered and intensely supercomputed prognostication is the weather forecast.
And we all know the accuracy of those ☺️

Multi-country forecasts of age distribution of deaths: Life expectancy and annuity valuation
Han Lin Shang, Steven Haberman
https://arxiv.org/abs/2507.04303

Multi-country forecasts of age distribution of deaths: Life expectancy and annuity valuation
We investigate two transformations within the framework of compositional data analysis for forecasting the age distribution of death counts. Drawing on age-specific period life-table death counts from 24 countries in the Human Mortality Database, we assess and compare the point and interval forecast accuracy of the two transformations. Enhancing the forecast accuracy of period life-table death counts holds significant value for demographers, who rely on such forecasts to estimate survival proba…

Hybrid Approach for Electricity Price Forecasting using AlexNet and LSTM
Bosubabu Sambana, Kotamsetty Geethika Devi, Bandi Rajeswara Reddy, Galeti Mohammad Hussain, Gownivalla Siddartha
https://arxiv.org/abs/2506.23504

Hybrid Approach for Electricity Price Forecasting using AlexNet and LSTM
The recent development of advanced machine learning methods for hybrid models has greatly addressed the need for the correct prediction of electrical prices. This method combines AlexNet and LSTM algorithms, which are used to introduce a new model with higher accuracy in price forecasting. Despite RNN and ANN being effective, they often fail to deal with forex time sequence data. The traditional methods do not accurately forecast the prices. These traditional methods only focus on demand and pr…

Optimizing Storytelling, Improving Audience Retention, and Reducing Waste in the Entertainment Industry
Andrew Cornfeld, Ashley Miller, Mercedes Mora-Figueroa, Kurt Samuels, Anthony Palomba
https://arxiv.org/abs/2506.00076

Optimizing Storytelling, Improving Audience Retention, and Reducing Waste in the Entertainment Industry
Television networks face high financial risk when making programming decisions, often relying on limited historical data to forecast episodic viewership. This study introduces a machine learning framework that integrates natural language processing (NLP) features from over 25000 television episodes with traditional viewership data to enhance predictive accuracy. By extracting emotional tone, cognitive complexity, and narrative structure from episode dialogue, we evaluate forecasting performance…

On the stability of global forecasting models
Marco Zanotti
https://arxiv.org/abs/2506.05776 https://arxiv.org/pdf/2506.05776

On the stability of global forecasting models
Forecast stability, that is the consistency of predictions over time, is essential in business settings where sudden shifts in forecasts can disrupt planning and erode trust in predictive systems. Despite its importance, stability is often overlooked in favor of accuracy, particularly in global forecasting models. In this study, we evaluate the stability of point and probabilistic forecasts across different retraining frequencies and ensemble strategies using two large retail datasets (M5 and V…