Efficient Learning of Time-series Function Approximation with Linear, LINE, or NKIST Algorithm


Efficient Learning of Time-series Function Approximation with Linear, LINE, or NKIST Algorithm – We propose a novel approach to time-dependent regression, based on a sequential learning algorithm to predict future times from data obtained from a predictive model. The causal models use an objective function to estimate the time between the time when the predicted time series are learned, and the causal models provide predictions in the space of time. The causal models can be regarded as either causal or predictive models, and we use them to learn causal models that include the causal model for the prediction and the predictive model for the prediction. Our proposed time-dependent (or causal-based) regression approach is evaluated on both simulated and real datasets. The results indicate that our method can generate causal models that are very accurate, as well as a large number of causal models that are not causal models.

In this paper we propose a general method, named Context-aware Temporal Learning (CTL), for extracting long-term dependencies across subnetworks from multi-task networks (MTNs) as well as in particular from multi-task networks. To understand why it is useful for this task, we examine the impact of two factors: (1) the structure of the MTN and the performance of the model; and (2) the number of training blocks. The results indicate that in this setting, we can achieve state-of-the-art performance, despite only using two large MTNs.

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Efficient Learning of Time-series Function Approximation with Linear, LINE, or NKIST Algorithm

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    Fast Learning of Multi-Task Networks for Predictive ModelingIn this paper we propose a general method, named Context-aware Temporal Learning (CTL), for extracting long-term dependencies across subnetworks from multi-task networks (MTNs) as well as in particular from multi-task networks. To understand why it is useful for this task, we examine the impact of two factors: (1) the structure of the MTN and the performance of the model; and (2) the number of training blocks. The results indicate that in this setting, we can achieve state-of-the-art performance, despite only using two large MTNs.


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