Linear, Machine Learning and Probabilistic Approaches for Time Series Analysis
arXiv:1703.01977v142 citations
Originality Synthesis-oriented
AI Analysis
This is an incremental study that benchmarks existing methods for time series analysis, relevant for researchers in forecasting and data science.
The paper compares linear, machine learning, and probabilistic methods for time series forecasting, presenting results from model combinations without specifying concrete numerical outcomes.
In this paper we study different approaches for time series modeling. The forecasting approaches using linear models, ARIMA alpgorithm, XGBoost machine learning algorithm are described. Results of different model combinations are shown. For probabilistic modeling the approaches using copulas and Bayesian inference are considered.