Predicting temperature trends with advanced deep learning techniques using LSTM. Weather forecasting is one of the most important tools in the modern world and developing a good temperature prediction model can be a huge competitive advantage for many businesses. Ambient temperature measurement is directly linked to several business areas such as agriculture, energy sector, trading, aviation, and many other sectors. By Octavio Santiago.
In this article, we will learn how to build LSTM Deep Learning models to forecast temperature precisely:
- The dataset used for training was taken from the INMET
- LSTM (Long Short-Term Memory) is a type of recurrent neural network (RNN) architecture
- Train and benchmarks
- LSTM Vanilla
- LSTM Stacked
The main outcome in training a Time Series model is how to split the dataset correctly into train and test datasets. Since the sequence is important we cannot split the dataset randomly, to split the dataset right, the sklearn function “TimeSeriesSplit” is being used. In the 48-hour prediction Stacked LSTM model showed a lower Average absolute error and a lower maximum temperature error as well, showing that adding complexity to the model was beneficial for these LSTM models and fitting data. Good read!
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