Combined Forecasting Model | ARIMA-LSTM Time Series Forecasting (Python)

Combined Forecasting Model | ARIMA-LSTM Time Series Forecasting (Python)

forecast result

basic introduction

ARIMA-LSTM time series forecasting (Python complete source code and data)
ARIMA-LSTM time series forecasting, AQI forecasting (Python complete source code and data)
combined model forecasting
ARIMA and LSTM are both classic models for time series forecasting. ARIMA is a statistical method based on differential and autoregressive moving average models, which can be used to capture trends and seasonality in time series. LSTM is a neural network-based model that can make predictions by learning long-term dependencies of time series.
Combining ARIMA and LSTM can form an ARIMA-LSTM hybrid model, which can make better use of the respective advantages of ARIMA and LSTM and improve the accuracy of time series forecasting.
Specifically, the implementation steps of the ARIMA-LSTM hybrid model are as follows:

1. Use the ARIMA model to preprocess the time series, including differencing the time series, determining the order of the ARIMA model, etc.;
2. Use the preprocessed time series as the input of the LSTM model, and train the LSTM model to predict the time series;
3. Combine the prediction results of the ARIMA model and the LSTM model to obtain the final time series prediction results.
   It should be noted that the ARIMA-LSTM hybrid model requires a lot of hyperparameter tuning, such as the order of the ARIMA model, the neural network structure and hyperparameters of the LSTM model, etc., which requires a lot of time and effort. At the same time, since the ARIMA and LSTM models are both black-box models, the results of the mixed model are also difficult to interpret, and certain model interpretation and visual analysis are required.

programming

• Complete source code and data download address: ARIMA-LSTM time series forecasting (Python)

References

[1] https://blog.csdn.net/m0_57362105/category_12075406.html?spm=1001.2014.3001.5482
[2] https://blog.csdn.net/m0_57362105/category_12075406.html?spm=1001.2014.3001.5482