R for SQListas (2): Forecasting the Future

@machinelearnbot

The less constrained model indeed performs better (judging by AIC, which drops from to 3696 to 3278). Autocorrelation of errors also is reduced overall. Now, with the improved models, let's finally get forecasting!


Relational Similarity Machines

arXiv.org Machine Learning

This paper proposes Relational Similarity Machines (RSM): a fast, accurate, and flexible relational learning framework for supervised and semi-supervised learning tasks. Despite the importance of relational learning, most existing methods are hard to adapt to different settings, due to issues with efficiency, scalability, accuracy, and flexibility for handling a wide variety of classification problems, data, constraints, and tasks. For instance, many existing methods perform poorly for multi-class classification problems, graphs that are sparsely labeled or network data with low relational autocorrelation. In contrast, the proposed relational learning framework is designed to be (i) fast for learning and inference at real-time interactive rates, and (ii) flexible for a variety of learning settings (multi-class problems), constraints (few labeled instances), and application domains. The experiments demonstrate the effectiveness of RSM for a variety of tasks and data.


Implement an ARIMA model using statsmodels (Python)

@machinelearnbot

In this article was written by Michael Grogan. Michael is a data scientist and statistician, with a profound passion for statistics and programming. In a previous tutorial, I elaborated on how an ARIMA model can be implemented using R. The model was fitted on a stock price dataset, with a (0,1,0) configuration being used for ARIMA. Here, I detail how to implement an ARIMA model in Python using the pandas and statsmodels libraries.


Implement an ARIMA model using statsmodels (Python)

@machinelearnbot

In this article was written by Michael Grogan. Michael is a data scientist and statistician, with a profound passion for statistics and programming. In a previous tutorial, I elaborated on how an ARIMA model can be implemented using R. The model was fitted on a stock price dataset, with a (0,1,0) configuration being used for ARIMA. Here, I detail how to implement an ARIMA model in Python using the pandas and statsmodels libraries.


Mental Sampling in Multimodal Representations

Neural Information Processing Systems

Both resources in the natural environment and concepts in a semantic space are distributed "patchily", with large gaps in between the patches. To describe people's internal and external foraging behavior, various random walk models have been proposed. In particular, internal foraging has been modeled as sampling: in order to gather relevant information for making a decision, people draw samples from a mental representation using random-walk algorithms such as Markov chain Monte Carlo (MCMC). However, two common empirical observations argue against people using simple sampling algorithms such as MCMC for internal foraging. First, the distance between samples is often best described by a Levy flight distribution: the probability of the distance between two successive locations follows a power-law on the distances.