The improved BP neural network algorithm prediction results are compared with the time series prediction
algorithm  and the least squares method , as shown in Fig.
Song, "Several Novel Dynamic Ensemble Selection Algorithms for Time Series Prediction
," Neural Processing Letters, 2018.
User physical activities are data with time series characteristics and the most representative time series prediction
methods are statistical regression methods and neural network methods.
 performed a multistep-ahead time series prediction
using multiple-output support vector regression.
Temperature series are time series data, and the commonly used methods of analyzing time series data were divided into traditional time series prediction
model and data-driven time series forecasting model .
Reference  proposed a novel hybrid wind power time series prediction
model to improve accuracy of ultra-short-term wind power forecasting.
When calculating the fractional-order calculus gray model, the minimum absolute error of the JC-1 monitoring point is 2.02 mm, appearing in the first half of monitoring time series prediction
, the maximum absolute error of it is 3.98 mm, appearing in the last phase of monitoring time series prediction
, and the front-to-rear difference reaches 1.96 mm; the minimum absolute error of the JC-2 monitoring point is 0.125 mm, appearing in the middle section of monitoring time series prediction
, the maximum absolute error of it is 0.709 mm, appearing in the last phase of monitoring time series prediction
, and the front-to-rear difference reaches 0.584 mm; the variation trends and laws of absolute errors of JC-1 and JC-2 monitoring points are basically the same.
Wei, "Neural network forecasting model for sunspots time series prediction
based on phase space reconstruction," Computer Simulation, vol.
The time series prediction
is one of the most important aspects in chaos theory.
Wang, "Chaotic time series prediction
method of algal bloom in urban lake and reservoir based on complex network," Chinese Patent, 201510128961.5P, 2015.
Jiao, "Traffic flow time series prediction
based on statistics learning theory," in Proceedings of the 5th IEEE International Conference on Intelligent Transportation Systems (ITSC '02), pp.
The SVR formulation for time series prediction
is expressed as follows.