lstm-models

There are 0 repository under lstm-models topic.

• ruthussanketh / natural-language-processing

  Codes, datasets, and explanations for some basic natural language tasks and models.

  lstm-modelsnatural-language-tasksnatural-language-processinglanguage-modelingcrfsuitenaive-bayes-classifierlstm-neural-networksstemminglemmatization
  Language:Jupyter Notebook 10

• lucylow / Yeezy-Taught-Me

  Yeezy Taught Me Text Generation. Training next character predictions RNN LSTM model with user input text corpus

  lstm-modelslstm-cellsrecurrent-neural-networksrnntext-corpustime-seriestime-series-analysistime-series-classificationtime-series-predictionspeech-recognitiontext-classificationtext-processingtenorflowneural-networklstmcharacter-predictioncharacter-generatorcorpusindexdb
  Language:JavaScript 9

• ecruhue / Stock-Data

  Stock price data is diverse and situational, and it is unlikely that any single model will be uniformly best across all industries or contexts. Based on our results, ARIMA GARCH methods are better for Consumer Discretionary and Financial industries, and LSTM models are better for Healthcare and Industrials. Specifically, we found Cumulative Year (CumYr) ARIMA GARCH performs best for the Consumer Discretionary industry, year by year (YrByYr) ARIMA GARCH performs best for Financials, YrByYr multivariate LSTM performs best for Healthcare, and YrByYr univariate LSTM performs best for Industrials. Overall, the LSTM models with YrByYr under multivariate condition perform better than LSTM models under other conditions.

  arima-garchlstm-modelsstock-pricestime-series
  Language:R 1

• davidtstill / Sentiment_Analysis_DJIA

  Analyzing daily news headlines to predict the direction of the Dow Jones Industrial Average

  lstm-modelsmachine-learningnews-headlinesrandom-forest
  Language:Jupyter Notebook 0