Methods And Techniques On The Short-term Forecasting Of Time Series

Time series is a series of observations obtained according to the time order. A lot of data is in the form of time series. From the economic, finance to engineering, from astronomy, geography to meteorology, from medicine to biology, and so on. All of these areas are related to time-series. For example, ship motion, the daily temperature, and the sequence weekly of the number of road accidents and so on. In the research fields of natural sciences and social sciences, a large number of decision-making can not be separated from prediction. Time series forecasting refers to the use of the historical observations of time series to predict the value at a future time. Time series forecasting is widely used, such as the weather forecasts, stock price index forecasts, extremely short-time prediction of ship motion and so on.In chapter 1 this paper introduces the research background of time series forecasting and the status at home and abroad. Chapter 2 presents detailed description of the basic theories on the five kinds of commonly used time series forecasting methods, including the linear forecasting methods (auto-regressive model AR, auto-regressive moving average model ARMA, and integrated autoregressive moving average model ARIMA) and the non-linear forecasting methods (radial basis function RBF neural network and BP network). In chapter 3, firstly, we implement these five kinds of methods for forecasting a wave signal, and experimentally compare the four kinds of error on these five forecasting methods, including bias, normalized bias, root mean square error and standard deviation. Furthermore, we compare the forecasting performances of these methods in different circumstances. Namely, when the prediction step increases, when take a different time period for forecasting, the magnitude of error for these five methods. Finally, we compare their performance on forecasting of the Uniform distribution and Gaussian distribution random sequence. In chapter 4, we develop a time series short-term forecasting system in the MATLAB GUIDE development platform, it covers the five kinds of forecasting methods above and we can easily predict time series on it. We are free to choose forecasting methods, data for forecasting and the prediction step, then the system can visually indicate the forecasting results and four kinds of error commonly used, it helps us in assessment and analysis of these forecasting methods. Finally, Chapter 5 summarizes the performances of these five forecasting methods and gives an outlook for their improvements.The main contributions of this paper are:(1) five kinds of time series forecasting methods are implemented in the MATLAB environment, and these methods are used on the short-term prediction on a wave signal and sequences satisfies Uniform distribution and Gaussian distribution. (2) make a comparison to the prediction steps, stability, prediction accuracy and running time of the five forecasting methods, summarize their advantages and disadvantages. (3) develop a short-term time series forecasting system in the MATLAB GUIDE development platform, easily use the five forecasting methods for time series forecasting.