Machine Learning and Forecasting: A Review

@inproceedings{Potgieter2020MachineLA,
  title={Machine Learning and Forecasting: A Review},
  author={Petrus H. Potgieter},
  year={2020}
}
The proliferation of business data and on-demand computing have propelled the use of artificial intelligence methods in quantitative forecasting. Machine learning has a prominent role in solving clustering and classification problems as well as dimensionality reduction. Nevertheless, traditional statistical methods of forecasting continue to perform well in competitions and many practical applications. The chapter considers critically the successes of machine learning in forecasting, using some… 
Interpretable Short-Term Electrical Load Forecasting Scheme Using Cubist
Daily peak load forecasting (DPLF) and total daily load forecasting (TDLF) are essential for optimal power system operation from one day to one week later. This study develops a Cubist-based
The Method of Constructing a Development Trajectory as the Basis of an Intelligent Module for Strategic Planning of the EPM System
TLDR
The description of the process of forming strategic goals for the company as procedure of the business process to form the company development program is presented and a method for constructing a development trajectory is proposed.

References

SHOWING 1-10 OF 36 REFERENCES
Machine Learning Strategies for Time Series Forecasting
TLDR
This chapter presents an overview of machine learning techniques in time series forecasting by focusing on three aspects: the formalization of one- step forecasting problems as supervised learning tasks, the discussion of local learning techniques as an effective tool for dealing with temporal data and the role of the forecasting strategy when the authors move from one-step to multiple-step forecasting.
Statistical and Machine Learning forecasting methods: Concerns and ways forward
TLDR
It is found that the post-sample accuracy of popular ML methods are dominated across both accuracy measures used and for all forecasting horizons examined, and that their computational requirements are considerably greater than those of statistical methods.
An Empirical Comparison of Machine Learning Models for Time Series Forecasting
TLDR
A large scale comparison study for the major machine learning models for time series forecasting, applying the models on the monthly M3 time series competition data to reveal significant differences between the different methods.
Machine Learning and AI for Risk Management
TLDR
Overall, this work presents an optimistic picture of the role of machine learning and AI in risk management, but note some practical limitations around suitable data management policies, transparency, and lack of necessary skillsets within firms.
Machine Learning: An Applied Econometric Approach
TLDR
This work presents a way of thinking about machine learning that gives it its own place in the econometric toolbox, and aims to make them conceptually easier to use by providing a crisper understanding of how these algorithms work, where they excel, and where they can stumble.
...
1
2
3
4
...