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Temperature Forecasting (LSTM & GRU)
LSTM and GRU models for weather time-series forecasting. Architecture comparison, sequence preprocessing, and performance evaluation.
Stack
What happened?
- 01
The question
Can a neural network really predict tomorrow's temperature from the past?
- 02
The problem
A temperature time series is non-stationary: seasonality, trends, noise. A naive model just repeats the previous value.
- 03
The hypothesis
Recurrent architectures LSTM and GRU, designed for temporal memory, can capture this structure better than a naive approach.
- 04
The build
Sequence preprocessing, LSTM and GRU training, architecture comparison, and honest performance evaluation.
- 05
The failure
GRU seemed to lose to LSTM… until I realised initialization and sequence length changed everything. Comparing without controlling variables is comparing wrongly.
- 06
The discovery
Sequence length and normalization matter as much as the architecture itself. Two models can differ for the wrong reasons.
- 07
The result
LSTM/GRU models trained and compared with honest evaluation of forecasting performance.
- 08
The next frontier
Predicting several horizons at once, adding exogenous variables (wind, pressure), and exploring temporal Transformers.