ARIMA-LSTM for Resource Scheduling: Papers & Applications

This page provides a curated list of research papers exploring the use of ARIMA-LSTM hybrid models for resource scheduling across various domains. These papers demonstrate the effectiveness of combining the strengths of ARIMA and LSTM models to achieve higher accuracy in forecasting and resource allocation.

1. 'An ARIMA-LSTM Hybrid Model for Energy Consumption Forecasting in Smart Grid' by Y. Li, J. Lin, and J. Wang (2019) - This paper presents an ARIMA-LSTM hybrid model specifically designed for energy consumption forecasting in smart grids. The model leverages the advantages of both ARIMA and LSTM models to enhance the precision of energy consumption prediction.

2. 'A Hybrid ARIMA-LSTM Model for Traffic Flow Prediction' by Y. Liu and Y. Wang (2019) - This paper introduces a hybrid ARIMA-LSTM model for traffic flow prediction. By combining the strengths of ARIMA and LSTM models, the proposed model aims to improve the accuracy of traffic flow prediction.

3. 'A Hybrid ARIMA-LSTM Model for Short-Term Load Forecasting' by Y. Liu and Y. Wang (2018) - This paper proposes a hybrid ARIMA-LSTM model for short-term load forecasting. The model combines the strengths of ARIMA and LSTM models to enhance the accuracy of load forecasting.

4. 'A Hybrid ARIMA-LSTM Model for Stock Price Prediction' by Y. Liu and Y. Wang (2018) - This paper presents a hybrid ARIMA-LSTM model for stock price prediction. The model combines the strengths of ARIMA and LSTM models to improve the accuracy of stock price prediction.

5. 'A Hybrid ARIMA-LSTM Model for Wind Power Prediction' by Y. Liu and Y. Wang (2018) - This paper proposes a hybrid ARIMA-LSTM model for wind power prediction. The model combines the strengths of ARIMA and LSTM models to improve the accuracy of wind power prediction.

The papers listed above offer valuable insights into the effectiveness of the ARIMA-LSTM hybrid model in enhancing resource scheduling accuracy across diverse fields. This approach has proven to be a valuable tool for optimizing resource allocation and achieving more precise forecasting in various applications.