User: TimeSeriesData_ML
Subject: LSTM vs Prophet for time series forecasting
Need to forecast electricity consumption for next 7 days based on historical data. Should I use LSTM neural network or Facebook Prophet? I have 2 years of hourly consumption data. Which approach will give better accuracy?

Reply by: MLengineer_Energy
Try both and compare. Prophet is much easier to use and works well for time series with strong seasonal patterns. LSTM can capture more complex patterns but requires more data preprocessing and hyperparameter tuning. With 2 years hourly data (17k+ data points) you have enough for LSTM. But start with Prophet as baseline, if accuracy is not satisfactory then try LSTM.

Reply by: DataScience_Consultant
Also consider simpler models like ARIMA or seasonal decomposition before jumping to deep learning. Sometimes classical methods work just as well with much less complexity. For electricity consumption, external factors like weather, holidays also matter. Make sure to include these features in your model. Feature engineering is more important than choice of algorithm.