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Found 8 papers, 2 papers with code
RollingLDA: An Update Algorithm of Latent Dirichlet Allocation to Construct Consistent Time Series from Textual Data
We propose a rolling version of the Latent Dirichlet Allocation, called RollingLDA.
Structural Periodic Vector Autoregressions
While seasonality inherent to raw macroeconomic data is commonly removed by seasonal adjustment techniques before it is used for structural inference, this approach might distort valuable information contained in the data.
Prototypes as Explanation for Time Series Anomaly Detection
Detecting abnormal patterns that deviate from a certain regular repeating pattern in time series is essential in many big data applications.
Lex2Sent: A bagging approach to unsupervised sentiment analysis
Unsupervised sentiment analysis is traditionally performed by counting those words in a text that are stored in a sentiment lexicon and then assigning a label depending on the proportion of positive and negative words registered.
A Bootstrap-Assisted Self-Normalization Approach to Inference in Cointegrating Regressions
Traditional inference in cointegrating regressions requires tuning parameter choices to estimate a long-run variance parameter.
Unsupervised Movement Detection in Indoor Positioning Systems of Production Halls
Beside its large volume, the analyzation of the resulting raw data is challenging due to the susceptibility towards noise.
Random boosting and random^2 forests -- A random tree depth injection approach
A Monte Carlo simulation, in which tree-shaped data sets with different numbers of final partitions are built, suggests that there are several scenarios where \emph{Random Boost} and \emph{Random$^2$ Forest} can improve the prediction performance of conventional hierarchical boosting and random forest approaches.
Improving Reliability of Latent Dirichlet Allocation by Assessing Its Stability Using Clustering Techniques on Replicated Runs
Based on the newly proposed measure for LDA stability, we propose a method to increase the reliability and hence to improve the reproducibility of empirical findings based on topic modeling.
