Time Series Forecasting Using Hybrid ARIMA and ANN Models Based on DWT Decomposition
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Summary
A novel technique of forecasting by segregating a time series dataset into linear and nonlinear components through DWT is suggested, which achieves best forecasting accuracies for each series.
- Type
- article
- Published
- 2015-01-01
- Cited by
- 270
- References
- 19
- Access
- Open access
- OpenAlex
- https://openalex.org/W385004530
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:60043226
Keywords
Autoregressive integrated moving average, Computer science, Series (stratigraphy), Discrete wavelet transform, Artificial neural network
References
- FORECASTING WITH ARTIFICIAL NEURAL NETWORKS: THE STATE OF THE ART
- A combination of artificial neural network and random walk models for financial time series forecasting
- Discrete wavelet transform-based time series analysis and mining
- A hybrid SARIMA wavelet transform method for sales forecasting
- Neural network forecasting for seasonal and trend time series
- Time series analysis, forecasting and control
- Combining Neural Network Forecasts on Wavelet-transformed Time Series
- Improving artificial neural networks' performance in seasonal time series forecasting
- Time-series forecasting through wavelets transformation and a mixture of expert models
- Forecasting stock markets using wavelet transforms and recurrent neural networks: An integrated system based on artificial bee colony algorithm
- Wavelet methods in (financial) time-series processing
- Time series forecasting using a hybrid ARIMA and neural network model
- Day-ahead electricity price forecasting using the wavelet transform and ARIMA models
- Neural Network Toolbox™ User's Guide
- A Hybrid Method Based on Wavelet Analysis for Short-term Load Forecasting
- Selecting Wavelet Transforms Model in Forecasting Financial Time Series Data Based on ARIMA Model
- Time Series Analysis, Forecasting and Control.
- Discrete Wavelet-Ann Approach in Time Series Flow Forecasting-A Case Study of Brahmaputra River PARESH CHANDRA DEKA, LATIFA HAQUE and ANIRUDDHA GOPAL BANHATTI
Cited by
- Efficient financial time series forecasting model using DWT decomposition
- Smart grid data analytics framework for increasing energy savings in residential buildings
- Demand forecasting based on natural computing approaches applied to the foodstuff retail segment
- Time series analytics using sliding window metaheuristic optimization-based machine learning system for identifying building energy consumption patterns
- An Effective Similarity Measure Algorithm for Time Series Based on Key Points
- A two-stage model for time series prediction based on fuzzy cognitive maps and neural networks
- Heterogeneous ensemble for power load demand forecasting
- Non-linear water level forecasting of Dungun river using hybridization of backpropagation neural network and genetic algorithm
- Multi-Scale Predictability for Emerging Foreign Exchange Markets
- ANN-Based Data Mining for Better Resource Management in the Next Generation Wireless Networks
- Understanding and Forecasting Stock Market Volatility Through Wavelet Decomposition, Statistical Learning and Econometric Methods
- The extent of virgin olive-oil prices’ distribution revealing the behavior of market speculators
- Testing the Weak-Form Efficiency of Ukrainian Stock Market
- Forecasting the Electric Energy Supply in Duhok Province using Proposed Methods Based on Wavelet Analysis and Sarima Methods
- İstatistiksel Metotlar ve Yapay Sinir Ağları Kullanarak Kısa Dönem Çok Adımlı Rüzgâr Hızı Tahmini
- Performance Analysis of Four Decomposition-Ensemble Models for One-Day-Ahead Agricultural Commodity Futures Price Forecasting
- Fractal Investigation and Maximal Overlap Discrete Wavelet Transformation (MODWT)-based Machine Learning Framework for Forecasting Exchange Rates
- A hybrid ETS-ANN model for time series forecasting
- Time Series Seasonal Analysis Based on Fuzzy Transforms
- Prediction of South China sea level using seasonal ARIMA models
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