Time-Series Prediction Algorithm

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A Time-Series Prediction Algorithm is a sequential prediction algorithm that can be applied by a temporal prediction system to solve a temporal prediction task.



References

2018a

2018b

2018c

2014

  • http://people.duke.edu/~rnau/411avg.htm
    • QUOTE: As a first step in moving beyond mean models, random walk models, and linear trend models, nonseasonal patterns and trends can be extrapolated using a moving-average or smoothing model. The basic assumption behind averaging and smoothing models is that the time series is locally stationary with a slowly varying mean. Hence, we take a moving (local) average to estimate the current value of the mean and then use that as the forecast for the near future. This can be considered as a compromise between the mean model and the random-walk-without-drift-model. The same strategy can be used to estimate and extrapolate a local trend. A moving average is often called a "smoothed" version of the original series because short-term averaging has the effect of smoothing out the bumps in the original series. By adjusting the degree of smoothing (the width of the moving average), we can hope to strike some kind of optimal balance between the performance of the mean and random walk models. The simplest kind of averaging model is the....

2013

2006