A Statistical Decomposition Based Neural Network For Multivariate Time Series Forecasting

Machine learning based time series forecasting methods are popular and can match the performance of statistical models, in terms of accuracy, scalability, speed, etc. This disclosure presents techniques that incorporate statistical modeling into a neural network framework. The hybrid time series forecasting model described herein is named Seasonality Trend AutoRegressive Residual Yeo-Johnson power transformation Neural Network (STARRY-N). STARRY-N combines the advantages of residual neural network structure (such as N-BEATS) and explainable statistical forecasting models (such as TBATS). The model utilizes a neural network structure with separate stacks for trend, power transformed trend, seasonality, residual correction, and covariate adoption such as holiday effects. STARRY-N has good accuracy and is an explainable forecasting model

A Comprehensive and Modularized Platform for Time Series Forecast and Analytics

Users that work with time series data typically disaggregate time series problems into various isolated tasks and use specific libraries, packages, tools, and services that deal with each individual task. However, the tools used are often fragmented. Analysts have to load different packages for common tasks such as data preprocessing, clustering, feature extraction, forecasting, hierarchical reconciliation, evaluation, and visualization. This disclosure describes a reliable, scalable infrastructure to meet various needs of time series practitioners without adding engineering overload. The infrastructure is modularized and the modules are connected in a flow type declarative language which makes the infrastructure extensible and future proof. Practitioners can use the entire infrastructure or only certain modules, while performing other operations using first or third party libraries or pipelines

Random quantiles of the Dirichlet process

We examine the finite-dimensional distributions of the random quantiles of a Dirichlet process and provide an exact decomposition of its bivariate version. While the Dirichlet distributions of probabilities are absolutely continuous, the corresponding distributions of quantiles are not.

Probability Elicitation, Scoring Rules, and Competition Among Forecasters

Probability forecasters who are rewarded via a proper scoring rule may care not only about the score, but also about their performance relative to other forecasters. We model this type of preference and show that a competitive forecaster who wants to do better than another forecaster typically should report more extreme probabilities, exaggerating toward zero or one. We consider a competitive forecaster's best response to truthful reporting and also investigate equilibrium reporting functions in the case where another forecaster also cares about relative performance. We show how a decision maker can revise probabilities of an event after receiving reported probabilities from competitive forecasters and note that the strategy of exaggerating probabilities can make well-calibrated forecasters (and a decision maker who takes their reported probabilities at face value) appear to be overconfident. However, a decision maker who adjusts appropriately for the misrepresentation of probabilities by one or more forecasters can still be well calibrated. Finally, to try to overcome the forecasters' competitive instincts and induce cooperative behavior, we develop the notion of joint scoring rules based on business sharing and show that these scoring rules are strictly proper.probability elicitation, scoring rules, forecasting competitions, probability forecasts, truthful revelation, overconfidence bias