Time series forecasting with a non-linear model and the scatter search meta-heuristic
Information Sciences: an International Journal
A New Intelligent System Methodology for Time Series Forecasting with Artificial Neural Networks
Neural Processing Letters
Autoregressive forecast of monthly total ozone concentration: A neurocomputing approach
Computers & Geosciences
Expert Systems with Applications: An International Journal
A Hybrid Intelligent Morphological Approach for Stock Market Forecasting
Neural Processing Letters
Seasonality and neural networks: a new approach
International Journal of Metaheuristics
Information Sciences: an International Journal
Expert Systems with Applications: An International Journal
Simulating wheat yield in New South Wales of Australia using interpolation and neural networks
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: models and applications - Volume Part II
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Forex trend classification using machine learning techniques
ACS'11 Proceedings of the 11th WSEAS international conference on Applied computer science
A robust automatic phase-adjustment method for financial forecasting
Knowledge-Based Systems
HAIS'10 Proceedings of the 5th international conference on Hybrid Artificial Intelligence Systems - Volume Part II
Neural network model for forecasting balkan stock exchanges
ICIC'11 Proceedings of the 7th international conference on Advanced Intelligent Computing
A comparison of univariate methods for forecasting container throughput volumes
Mathematical and Computer Modelling: An International Journal
A simulation-based planning system for wind turbine construction
Proceedings of the Winter Simulation Conference
Combining motif information and neural network for time series prediction
International Journal of Business Intelligence and Data Mining
A Morphological-Rank-Linear evolutionary method for stock market prediction
Information Sciences: an International Journal
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Forecasting of time series that have seasonal and other variations remains an important problem for forecasters. This paper presents a neural network (NN) approach to forecasting quarterly time series. With a large data set of 756 quarterly time series from the M3 forecasting competition, we conduct a comprehensive investigation of the effectiveness of several data preprocessing and modeling approaches. We consider two data preprocessing methods and 48 NN models with different possible combinations of lagged observations, seasonal dummy variables, trigonometric variables, and time index as inputs to the NN. Both parametric and nonparametric statistical analyses are performed to identify the best models under different circumstances and categorize similar models. Results indicate that simpler models, in general, outperform more complex models. In addition, data preprocessing especially with deseasonalization and detrending is very helpful in improving NN performance. Practical guidelines are also provided.