Prado R, West M (2010). Time Series: Modeling, Computation, and Inference. Chapman & Hall/CRC Press, London.Prado, R. and West, M. (2010). Time Series: Modeling, Computation & In- ference. Chapman & Hall/CRC Pr
The Fisher information matrix is useful in time series modeling mainly because the significance of estimated parameters can also be derived from it. It can also be used in iterative procedures of parameter estimation. The paper is mainly concerned with algorithmic aspects related to the computation ...
In the computation of the complexity-penalized KL criterion, we replace the multivariate time series by the SLEX principal components. Thus, the model selection procedure is conducted by taking into account the full information on the multivariate spectra. 3. On the necessity of the complexity ...
Automatic time series modeling, intervention analysis, and effective forecasting - Guerard - 1989J. B. Guerard, "Automatic time series modeling, intervention analysis, and effective forecasting,"Journal of Statistical Computation and Simulation., vol. 34, no. 1, pp. 43-49, 1988....
In the first type of time series problem, you would like to predict future values of a time seriesy(t) from past values of that time series and past values of a second time seriesx(t). This form of prediction is called nonlinear autoregressive with exogenous (external) input, or NARX ...
deep learning; continual learning; sensor; time series; preprocessing; non-stationary; catastrophic forgetting1. Introduction Time series modeling is a challenging task in data mining and machine learning. Popular time series modeling tasks include classification, anomaly detection, regression, forecasting,...
The major contributions of this paper are: First, we propose an LSTM-RNN model as an FDP model that accurately predicts distress two years ahead. The LSTM is one of the most advanced DL architectures for capturing long-term dependencies from financial time-series data. In order to improve the...
FEDformer - FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecasting [ICML 2022] [Code]. Pyraformer - Pyraformer: Low-complexity Pyramidal Attention for Long-range Time Series Modeling and Forecasting [ICLR 2022] [Code]. Autoformer - Autoformer: Decomposition Transformer...
The final step is to use the time series for modeling. To show an example, in our recent conference paper, we similarly extracted the sentiment from headlines of research articles published in the top 5 economic journals. Then, we use rolling time-varying correlations of a 5-year window and...
This model is a temporal convolutional network (TCN), that applies common imaging task methods to time-series modeling. One-dimensional "causal" convolutions form the backbone of the network and enable the model to learn complex patterns over long durations in the training history. For more ...