LSTM for Time Series Forecasting
Sequence preparation, stateless vs stateful LSTM, look-back window, multi-step forecasting.
Why LSTM for Time Series?
LSTM (Long Short-Term Memory) is a recurrent neural network that remembers patterns across long sequences using gated memory cells. It can model nonlinear dependencies that ARIMA cannot, making it powerful for complex series given enough data.
Sequence Windowing
LSTMs learn from windows: fixed-length slices of past steps used to predict the next value. With a window of 10, the model sees steps 1-10 to predict step 11, then 2-11 to predict 12, and so on.
All lessons in this course
- Time Series Components and Stationarity
- ARIMA and SARIMA Models
- Prophet for Automated Forecasting
- LSTM for Time Series Forecasting