Univariate Time Series Forecasting by Investigating Intermittence and Demand IndividuallyIntermittent time series forecasting is a challenging task which still needs particular attention of researchers. The more unregularly events occur, the more difficult is it to predict them. With Croston's approach ...
Chapter5 Univariatetimeseriesmodellingandforecasting 5-2 1introduction •单变量时间序列模型 –只利用变量的过去信息和可能的误差项的当前和过去值来建模和预测的一类模型(设定)。–与结构模型不同；通常不依赖于经济和金融理论–用于描述被观测数据的经验性相关特征 •ARIMA（AutoRegressiveIntegratedMovingAverage）是...
In this tutorial, you will discover how to develop a suite of deep learning models for univariate time series forecasting. After completing this tutorial, you will know: How to develop a robust test harness using walk-forward validation for evaluating the performance of neural network models....
5-1 Chapter5 Univariatetimeseries modellingandforecasting 5-2 1introduction •单变量时间序列模型–只利用变量的过去信息和可能的误差项的当前和过去值来建模和预测的一类模型(设定)。–与结构模型不同;通常不依赖于经济和金融理论–用于描述被观测数据的经验性相关特征•ARIMA(AutoRegressiveIntegratedMovingAverage)...
Shah, C. (1997), 'Model selection in univariate time series forecasting using discriminant analysis', International Journal of Forecasting 13, 489-500.C. Shah, Model selection in univariate time series forecasting using discriminant analysis, Int. J. Forecasting 13 (1997) 489-500....
(2021) a comparison between the performance of traditional NN and SNN for financial time-series forecasting is performed. However, regarding SNN there is a lack of information concerning the encoding method, the learning approach, the structure of the network and the neuron model, impeding ...
Forecasting performances of three methods--Box-Jenkins, Holt-Winters and stepwise autoregression--are compared over a large sample of economic time series. The possibility of combining individual forecasts in the production of an overall forecast is explored, and we present empirical results which ...
Most forecasting methods use recent past observations (lags) to model the future values of univariate time series. Selecting an adequate number of lags is important for training accurate forecasting models. Several approaches and heuristics have been devised to solve this task. However, there is no...
These models are capable of representing both stationary and nonstationary series and can be easily generalized to allow for seasonality and to accommodate a leading indicator. In addition a model building strategy is discussed and the problem of forecasting is considered. Finally, the methodology is...
Model selection in univariate time series forecasting using discriminant analysis When a large number of time series are to be forecast on a regular basis, as in large scale inventory management or production control, the appropriate cho... C Shah - 《International Journal of Forecasting》 被引量...