Autoregressive forests for multivariate time series modeling

By K. Tuncel, Mustafa Gokce Baydogan. 2018

93 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Economics and Econometrics, Signal Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Houshang Darabi
2. Fazle Karim
3. Samuel Harford
4. Somshubra Majumdar

In Economics and Econometrics

1. Houshang Darabi
2. Fazle Karim
3. Samuel Harford
4. Somshubra Majumdar

In Signal Processing

1. Houshang Darabi
2. Fazle Karim
3. Samuel Harford
4. Somshubra Majumdar

Citations in the corpus

• AdaRNN: Adaptive Learning and Forecasting of Time Series (2021, unknown): “Traditional approaches such as hidden Markov models (HMMs) [43], dynamic Bayesian networks [35], Kalman filters [9], and other statistical models (e.”
• Multivariate LSTM-FCNs for Time Series Classification (2018, methodology): “Auto-Regressive forests for multivariate time series modelling (mv-ARF) [21] uses a tree ensemble.”