The damped trend method of exponential smoothing is a benchmark that has been difficult to beat in empirical studies of forecast accuracy. One explanation for this success is the flexibility of the method, which contains a variety of special cases that are automatically selected during the fitting process. That is, when the method is fitted, the optimal parameters usually define a special case ...

Understanding the complexity of urban expansion requires an analysis of the factors influencing the spatial and temporal processes of rural-urban land conversion. This study aims at building a statistical land conversion model to assist in understanding land use change patterns. Specifically, GIS coupled with a logistic regression model and exponential smoothing techniques is used for exploring...

Abstract The aim of this paper is to present a set Python-based tools develop forecasts using time series data sets. material based on 4-week course that the author has taught for 7 years students operations research, management science, analytics, and statistics 1-year MSc programmes. However, it can easily be adapted various other audiences, including executive or some undergraduate No partic...

in this paper, we propose a new residual analysis method using fourier series transform into fuzzy time series model for improving the forecasting performance. this hybrid model takes advantage of the high predictable power of fuzzy time series model and fourier series transform to fit the estimated residuals into frequency spectra, select the low-frequency terms, filter out high-frequency term...

Procedures for estimation of the parameters in linear models allow for missing observations in the dependent and independent variables. In serially correlated data, an alternative approach to model the data is to use time series analysis techniques. However, generally available programs for Autoregressive Integrated Moving Average (ARIMA) time series analysis assume measurements at fixed interv...