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Time Series Prediction

Some details specific to time series:

  • Feature engineering is difficult and should be handled by care (can easily learn bias - i.e. trends, seasonality, accumulation)
  • Data is much smaller
  • A different algorithm is needed
    • extrapolation
    • confidence interval
  • Residuals should represent white noise
    • no correlation
    • zero mean and constant variance
  • Resolution matters
    • prepare data and predict very granular then predictions can be aggregated, for example: predict use and predict hourly then take average for daily
  • Confidence intervals:
    • keep some part of the data aka. dropout and run simulations
    • kernel mixture network
  • Some models might have bias
    • have high accurary or high error (black or gray swan according to the nature of the series)

Models to be tested in the order of complexity (to have a baseline and to iterate):

  • simple hand-made formula: average the data
  • statistical model: autoregression such as SARIMA, linear sum of old observations
  • exponential smoothing or winter-holt: this time exponential functions instead of linear, forget old observations at an exponentially decreasing reate
  • simple neural network
  • advanced neural network: LSTM, GRU, etc.
Last updated on 11/10/2019 by ferhat elmas
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