Learning Based Forecasting

Learning-based forecasting leverages machine learning to predict future values in time series data, aiming to improve accuracy and efficiency compared to traditional methods. Current research emphasizes automating the forecasting process, developing codeless frameworks for easier implementation, and enhancing model adaptability through techniques like algorithm selection "racks" that dynamically choose the best model for a given context. This field is significant for its potential to improve decision-making across diverse sectors, from retail and energy management to financial forecasting, by providing more accurate and reliable predictions with reduced computational overhead.

Papers

September 26, 2023

Telescope: An Automated Hybrid Forecasting Approach on a Level-Playing Field
André Bauer, Mark Leznik, Michael Stenger, Robert Leppich, Nikolas Herbst, Samuel Kounev, Ian Foster
State of the Art Forecasting Forecasting Method Forecast Combination Learning Based Forecasting Event Horizon Telescope

July 28, 2023

DeepTSF: Codeless machine learning operations for time series forecasting
Sotiris Pelekis, Evangelos Karakolis, Theodosios Pountridis, George Kormpakis, George Lampropoulos, Spiros Mouzakitis, Dimitris Askounis
Machine Learning Time Series Forecasting Load Forecasting Economic Operation Learning Based Forecasting

June 12, 2023

Making forecasting self-learning and adaptive -- Pilot forecasting rack
Shaun D'Souza, Dheeraj Shah, Amareshwar Allati, Parikshit Soni
Forecasting Performance Demand Prediction Demand Forecasting Learning Based Forecasting

May 11, 2023

Implicitly normalized forecaster with clipping for linear and non-linear heavy-tailed multi-armed bandits
Yuriy Dorn, Nikita Kornilov, Nikolay Kutuzov, Alexander Nazin, Eduard Gorbunov, Alexander Gasnikov
Heavy Tailed Linear Ordered Data Implicit Language Forecast Utterance Heavy Tailed Reward Adversarial Multi Armed Bandit Learning Based Forecasting

February 14, 2023

Masked Multi-Step Probabilistic Forecasting for Short-to-Mid-Term Electricity Demand
Yiwei Fu, Nurali Virani, Honggang Wang
Mid Term Electricity Learning Based Forecasting

November 9, 2022

On the use of learning-based forecasting methods for ameliorating fashion business processes: A position paper
Geri Skenderi, Christian Joppi, Matteo Denitto, Marco Cristani
Greater Public Use Forecasting Task Forecasting Method Fashion Image Editing Learning Based Forecasting

July 7, 2022

HierarchicalForecast: A Reference Framework for Hierarchical Forecasting in Python
Kin G. Olivares, Federico Garza, David Luo, Cristian Challú, Max Mergenthaler, Souhaib Ben Taieb, Shanika L. Wickramasuriya, Artur Dubrawski
Baseline Model Hierarchical Forecasting Reference Model Learning Based Forecasting Forecast Aggregation

June 17, 2022

StaDRe and StaDRo: Reliability and Robustness Estimation of ML-based Forecasting using Statistical Distance Measures
Mohammed Naveed Akram, Akshatha Ambekar, Ioannis Sorokos, Koorosh Aslansefat, Daniel Schneider
Machine Learning Robustness Evaluation Reliability Evaluation Learning Based Forecasting

Learning Based Forecasting

Papers

Telescope: An Automated Hybrid Forecasting Approach on a Level-Playing Field

DeepTSF: Codeless machine learning operations for time series forecasting

Making forecasting self-learning and adaptive -- Pilot forecasting rack

Implicitly normalized forecaster with clipping for linear and non-linear heavy-tailed multi-armed bandits

Masked Multi-Step Probabilistic Forecasting for Short-to-Mid-Term Electricity Demand

On the use of learning-based forecasting methods for ameliorating fashion business processes: A position paper

HierarchicalForecast: A Reference Framework for Hierarchical Forecasting in Python

StaDRe and StaDRo: Reliability and Robustness Estimation of ML-based Forecasting using Statistical Distance Measures