Are deep learnings models better than classical models for timeseries forecasting?

Introduction

Time series forecasting is a critical task in various domains such as finance, weather prediction, and energy load forecasting. The advent of deep learning has introduced new methodologies that promise to outperform classical models. This synthesis aims to evaluate whether deep learning models are superior to classical models for time series forecasting.

Key Insights

Superior Performance of Deep Learning Models:
- Deep learning models, such as LSTM and TCN, have shown better performance in weather forecasting compared to classical models like ARIMA and VAR.
- Deep neural networks combined with multiresolution analysis outperform traditional models in stock market forecasting.
- A sequence-to-sequence deep learning framework for multivariate time series forecasting demonstrated better accuracy than classical shallow learning models.
Hybrid Models Combining Deep Learning and Classical Techniques:
- Hybrid models that integrate deep learning (e.g., LSTM) with classical methods (e.g., exponential smoothing) have shown high performance in mid-term electric load forecasting, outperforming standalone classical models.
- A hybrid framework combining LSTM with empirical wavelet transform and other optimization techniques achieved superior prediction accuracy in stock market forecasting.
Probabilistic Forecasting with Deep Learning:
- DeepAR, a deep learning-based autoregressive recurrent network, has shown significant accuracy improvements (around 15%) over state-of-the-art classical methods in probabilistic forecasting.
Challenges and Considerations:
- While deep learning models are more expressive, they can struggle with non-stationarity and lack of inductive biases, which classical models handle better. However, advanced deep learning models like DeepTIMe address these issues effectively.

Conclusion

Deep learning models generally outperform classical models in time series forecasting across various domains, including weather, stock market, and energy load forecasting. Hybrid models that combine deep learning with classical techniques further enhance forecasting accuracy. Despite some challenges, advanced deep learning models have shown significant improvements over classical methods, making them a preferable choice for time series forecasting.

Try another search

The potential of smart materials in creating adaptive and responsive environments.

Is Electro stimulatio better then strenght training

why ngo are engaged in women empowerment

The ethics and implications of predictive analytics in insurance and healthcare.

online partial discharge sensors

How do computers store and process information?

Are deep learnings models better than classical models for timeseries forecasting?

Summary

Introduction

Key Insights

Conclusion

Sources and full results

DeepTIMe: Deep Time-Index Meta-Learning for Non-Stationary Time-Series Forecasting

DeepTIMe is an efficient and accurate model for non-stationary time-series forecasting, outperforming state-of-the-art methods and achieving competitive results on real-world datasets.

Combining Deep Learning and Multiresolution Analysis for Stock Market Forecasting

Combining deep neural networks with multiresolution analysis significantly improves stock market forecasting accuracy compared to traditional stationary wavelet transform models.

A Hybrid Residual Dilated LSTM end Exponential Smoothing Model for Mid-Term Electric Load Forecasting

The hybrid deep learning model combining exponential smoothing, LSTM, and ensembling effectively forecasts mid-term electric load, outperforming classical and state-of-the-art models.

DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks

DeepAR, a deep learning-based method for probabilistic forecasting, improves accuracy by around 15% compared to state-of-the-art methods in real-world scenarios.

Deep learning-based effective fine-grained weather forecasting model

The proposed lightweight deep learning model using LSTM and TCN models outperforms the complex WRF model in providing efficient and accurate weather forecasts up to 12 hours.

Time Series Forecasting Using Sequence-to-Sequence Deep Learning Framework

The proposed sequence-to-sequence deep learning framework improves air quality forecasting accuracy compared to classic shallow learning and baseline deep learning models.

An improved deep learning model for predicting stock market price time series

The novel stock closing price forecasting framework, using LSTM networks and dropout strategy, achieves higher prediction accuracy than traditional models, making it suitable for stock market monitoring and financial data analysis.

Deep Learning for Photovoltaic Power Plant Forecasting

Deep Learning models improve the accuracy of photovoltaic power plant output forecasting compared to classical Artificial Neural Networks.

Improving the Accuracy of Global Forecasting Models using Time Series Data Augmentation

Our data augmentation framework significantly improves the accuracy of Global Forecasting Models in less data-abundant settings, outperforming state-of-the-art univariate forecasting methods.

A novel validation framework to enhance deep learning models in time-series forecasting

Our novel validation framework significantly improves the forecasting performance of deep learning models in time-series forecasting, enhancing efficiency, accuracy, and reliability.

Try another search