Time series forecasting using a hybrid ARIMA and neural network model
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Summary
Experimental results with real data sets indicate that the combined model can be an effective way to improve forecasting accuracy achieved by either of the models used separately.
- Type
- article
- Published
- 2003-01-01
- Cited by
- 4,248
- References
- 55
- OpenAlex
- https://openalex.org/W2117014758
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:14166978
Keywords
Autoregressive integrated moving average, Artificial neural network, Computer science, Time series, Series (stratigraphy)
References
- Experience with Forecasting Univariate Time Series and the Combination of Forecasts
- An Introduction to Bispectral Analysis and Bilinear Time Series Models
- Exchange rate models of the seventies. Do they fit out of sample
- FORECASTING WITH ARTIFICIAL NEURAL NETWORKS: THE STATE OF THE ART
- Time series modelling of water resources and environmental systems
- Insights into neural-network forecasting of time series corresponding to ARMA(p,q) structures
- The Impact of Empirical Accuracy Studies On Time Series Analysis and Forecasting
- Artificial neural networks as applied to long-term demand forecasting
- Invited review combining forecasts—twenty years later
- To combine or not to combine? Issues of combining forecasts
- Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation
- The Combination of Forecasts
- A hybrid econometric—neural network modeling approach for sales forecasting
- Combining forecasts: A review and annotated bibliography
- What is the ‘best’ method of forecasting?
- Model uncertainty and forecast accuracy
- Why combining works
- General exponential smoothing and the equivalent arma process
- An introduction to bilinear time series models
- Some recent developments in non-linear time series modelling, testing, and forecasting☆
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- Nonlinear Prediction of The Standard & Poor's 500 and The Hang Seng Index under A Dynamic Increasing Sample
- Use of numerical weather forecast and time series models for predicting reference evapotranspiration
- Time-Series Forecast with Adaptive Feedback Controlled Predictor
- TIME SERIES FORECASTING WITH MULTIPLE CANDIDATE MODELS: SELECTING OR COMBINING?
- An improved hybrid of nonlinear autoregressive with exogenous input and autoregressive moving average for long-term machine state forecasting based on vibration signal
- Fitting precipitation variability in Dobrudja region
- Nonparametric models for the regional precipitation evolution in Dobrudja
- Price spike forecasting in a competitive day-ahead energy market
- Forex trend classification using machine learning techniques
- The GMDH model and its application to forecating of rice yields
- A novel three-step procedure to forecast the inspection volume
- Opportunities to Improve the Competitiveness of Romanian Organizations
- Time Series Forecasting Using Hybrid ARIMA and ANN Models Based on DWT Decomposition
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