Distributed ARIMA models for ultra-long time series

Iterative ARIMA-multiple support vector regression models for long term time series prediction

Support Vector Regression (SVR) has been widely applied in time series forecasting. Considering long term predictions, iterative predictions perform many one-step-ahead predictions until the desired horizon is achieved. This process accumulates the error from previous predictions and may affect the quality of forecasts. In order to improve long term iterative predictions a hybrid multiple Autor...

Short-Term Forecasting with ARIMA Time Series Models

Online ARIMA Algorithms for Time Series Prediction

Autoregressive integrated moving average (ARIMA) is one of the most popular linear models for time series forecasting due to its nice statistical properties and great flexibility. However, its parameters are estimated in a batch manner and its noise terms are often assumed to be strictly bounded, which restricts its applications and makes it inefficient for handling large-scale real data. In th...

improving the hybrid anns/arima models with probabilistic neural networks (pnns) for time series forecasting

time series forecasting is an active research area that has drawn considerable attention for applications in a variety of areas. forecasting accuracy is one of the most important features of forecasting models. nowadays, despite the numerous time series forecasting models which have been proposed in several past decades, it is widely recognized that financial markets are extremely difficult to ...

Long memory time series models

For a long time the most frequently used models in time series analysis were the AR, MA and ARMA processes. Their spectral densities are continuous and therefore bounded functions on [ — n, it]. If the periodogram of real data reached significantly high values, it was considered as an indication of the trend or of a periodic component. The bias arising after trend removal in the spectral densit...