Mastering Weather Predictions: Unleash the Power of AI with LSTM Deep Learning Models for Accurate Temperature Forecasts | by Octavio Santiago | Mar, 2023
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.
Measuring temperature is an important aspect of weather forecasting and is one of the most fundamental and widely-used indicators of climate conditions because it influences the behavior of atmospheric gases and the circulation of air and water, which are critical components of the climate system.
Some key ways in which weather temperature measurement is important for many businesses are:
- Agriculture: Temperature measurement is used to monitor growing conditions and predict potential crop yields, which helps farmers to make informed decisions about planting, harvesting, and storage of crops.
- Energy and utilities: Temperature measurement is used to forecast heat waves, cold snaps, and other temperature extremes, which helps utilities to plan for and respond to changes in energy demand. Some companies trade energy and energy demand prediction is crucial to get good profit.
- Transportation: Temperature measurement is used to predict and monitor extreme temperatures, such as heat waves or cold snaps, which can affect the safety and efficiency of transportation systems such as roads and airports.
Overall, temperature measurement is a fundamental tool for understanding and predicting weather and climate conditions, a model that can predict the temperature precisely is critical for the functioning of many different industries and very advantageous for many others.
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 website with meteorological data from the station SAO PAULO — INTERLAGOS (A771) in the city of São Paulo — Brazil. The sampling frequency of the temperature data is hourly and 1 year of training from 23-March-2022 01:00 to 23-March-2023 12:00 was used.
Source: https://mapas.inmet.gov.br/#
- Target Variable: Temp Max (ºC)
LSTM (Long Short-Term Memory) is a type of recurrent neural network (RNN) architecture that is particularly well-suited for processing sequential data, such as time series, speech, or text.
Unlike traditional feedforward neural networks, the basic building block of an LSTM model is the LSTM cell, which is designed to remember and update information over a long period of time. Each LSTM cell has a set of “gates” that control the flow of information into and out of the cell, allowing the network to selectively store or forget information as it processes the input data.
An LSTM network is typically composed of multiple LSTM cells arranged in a sequence. The input data is passed through the cells one time step at a time, with the output of one cell being used as the input to the next cell. This allows the network to learn and model complex sequential patterns in the data.
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.
LSTM vanilla (or “vanilla LSTM”) refers to a type of Long Short-Term Memory (LSTM) neural network architecture that is a basic or standard version of the LSTM model.
Train
- 18 days of sample time
- Dataset hourly separated
- 80% train dataset
Regression metrics for the training:
- MAE (Mean Absolute Error): 0.53
- MSE (Mean Squared Error) : 0.60
- MAPE (Mean Absolute Percentage Error): 2,9 %
Problems:
- Delay for temperature rises and peak identification
Test
Regression metrics for the training:
- MAE (Mean Absolute Error): 0.59
- MSE (Mean Squared Error) : 0.82
- MAPE (Mean Absolute Percentage Error): 2,5 %
Forecasting
- Forecasting 48 hours ahead
Stacked LSTM (Long Short-Term Memory) is a variant of the popular LSTM recurrent neural network architecture. In a standard LSTM, a single layer of LSTM cells is used to process sequential data. In a stacked LSTM, multiple layers of LSTM cells are used, with the output of one layer being used as the input to the next layer.
The stacked LSTM architecture is used to improve the ability of the network to learn and model complex sequential patterns in data. Each layer in the stack can learn different levels of abstraction and extract features from the input data, which allows the network to capture more complex relationships and dependencies in the data.
Train
- 18 days of sample time
- Dataset hourly separated
- 80% train dataset
Regression metrics for the training:
- MAE (Mean Absolute Error): 0.52
- MSE (Mean Squared Error) : 0.57
- MAPE (Mean Absolute Percentage Error): 2,8 %
Problems:
- Delay for temperature rises and peak identification
- Problem with future predictions
Test
Regression metrics for the training:
- MAE (Mean Absolute Error): 0.57
- MSE (Mean Squareed Error) : 0.82
- MAPE (Mean Absolute Percentage Error): 2,4 %
Forecasting
- Forecasting 48 hours ahead
After the training and tests performed for each model, the most consistent method to compare their efficiency is to compare to new and real data. A prediction for the next 48 hours was performed for LSTM Vanilla and Stacked and compared to real data (yellow) below:
LSTM Vanilla
- Average error: 2,04 ºC
- Maximum error: 4,44ºC
LSTM Stacked
- Average error: 1,36 ºC
- Maximum error: 3,16ºC
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.
In the 48-hour prediction, the Stacked LSTM model exhibited a reduced average absolute error and maximum temperature error compared to the Vanilla. These findings indicate that the inclusion of additional complexity in the model was advantageous for the LSTM architecture. The introduction of these additional layers allows the model to capture more complex patterns and dependencies in the data which can enhance its performance in prediction tasks. The observed decrease in errors suggests that the added complexity enabled the model to capture more of the underlying patterns and variability in the temperature data, resulting in more accurate predictions.
Both models exhibited a high degree of precision in predicting temperature values for the next 48 hours, with an average error rate of approximately 2% and 2ºC. However, a challenge encountered by both models was accurately identifying a secondary temperature valley that differed from the expected outcome. This issue highlights a potential limitation in the models’ ability to capture and interpret complex patterns in the temperature data, particularly in cases where unexpected fluctuations or anomalies occur. Further research may be necessary to improve the models’ sensitivity to such irregularities and enhance their overall performance for temperature prediction.
Thank you so much for reading! Any questions or suggestions please contact me via LinkedIn: https://www.linkedin.com/in/octavio-b-santiago/
If you want to implement this solution or learn more about LSTM algorithms you can find the full python code on my GitHub repo in the link below:
References
Datasource: https://mapas.inmet.gov.br/# — INMET (Instituto Nacional de Meteorologia) Public Dataset : CC0 1.0 Universal License (https://portal.inmet.gov.br/sobre)
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.
Measuring temperature is an important aspect of weather forecasting and is one of the most fundamental and widely-used indicators of climate conditions because it influences the behavior of atmospheric gases and the circulation of air and water, which are critical components of the climate system.
Some key ways in which weather temperature measurement is important for many businesses are:
- Agriculture: Temperature measurement is used to monitor growing conditions and predict potential crop yields, which helps farmers to make informed decisions about planting, harvesting, and storage of crops.
- Energy and utilities: Temperature measurement is used to forecast heat waves, cold snaps, and other temperature extremes, which helps utilities to plan for and respond to changes in energy demand. Some companies trade energy and energy demand prediction is crucial to get good profit.
- Transportation: Temperature measurement is used to predict and monitor extreme temperatures, such as heat waves or cold snaps, which can affect the safety and efficiency of transportation systems such as roads and airports.
Overall, temperature measurement is a fundamental tool for understanding and predicting weather and climate conditions, a model that can predict the temperature precisely is critical for the functioning of many different industries and very advantageous for many others.
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 website with meteorological data from the station SAO PAULO — INTERLAGOS (A771) in the city of São Paulo — Brazil. The sampling frequency of the temperature data is hourly and 1 year of training from 23-March-2022 01:00 to 23-March-2023 12:00 was used.
Source: https://mapas.inmet.gov.br/#
- Target Variable: Temp Max (ºC)
LSTM (Long Short-Term Memory) is a type of recurrent neural network (RNN) architecture that is particularly well-suited for processing sequential data, such as time series, speech, or text.
Unlike traditional feedforward neural networks, the basic building block of an LSTM model is the LSTM cell, which is designed to remember and update information over a long period of time. Each LSTM cell has a set of “gates” that control the flow of information into and out of the cell, allowing the network to selectively store or forget information as it processes the input data.
An LSTM network is typically composed of multiple LSTM cells arranged in a sequence. The input data is passed through the cells one time step at a time, with the output of one cell being used as the input to the next cell. This allows the network to learn and model complex sequential patterns in the data.
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.
LSTM vanilla (or “vanilla LSTM”) refers to a type of Long Short-Term Memory (LSTM) neural network architecture that is a basic or standard version of the LSTM model.
Train
- 18 days of sample time
- Dataset hourly separated
- 80% train dataset
Regression metrics for the training:
- MAE (Mean Absolute Error): 0.53
- MSE (Mean Squared Error) : 0.60
- MAPE (Mean Absolute Percentage Error): 2,9 %
Problems:
- Delay for temperature rises and peak identification
Test
Regression metrics for the training:
- MAE (Mean Absolute Error): 0.59
- MSE (Mean Squared Error) : 0.82
- MAPE (Mean Absolute Percentage Error): 2,5 %
Forecasting
- Forecasting 48 hours ahead
Stacked LSTM (Long Short-Term Memory) is a variant of the popular LSTM recurrent neural network architecture. In a standard LSTM, a single layer of LSTM cells is used to process sequential data. In a stacked LSTM, multiple layers of LSTM cells are used, with the output of one layer being used as the input to the next layer.
The stacked LSTM architecture is used to improve the ability of the network to learn and model complex sequential patterns in data. Each layer in the stack can learn different levels of abstraction and extract features from the input data, which allows the network to capture more complex relationships and dependencies in the data.
Train
- 18 days of sample time
- Dataset hourly separated
- 80% train dataset
Regression metrics for the training:
- MAE (Mean Absolute Error): 0.52
- MSE (Mean Squared Error) : 0.57
- MAPE (Mean Absolute Percentage Error): 2,8 %
Problems:
- Delay for temperature rises and peak identification
- Problem with future predictions
Test
Regression metrics for the training:
- MAE (Mean Absolute Error): 0.57
- MSE (Mean Squareed Error) : 0.82
- MAPE (Mean Absolute Percentage Error): 2,4 %
Forecasting
- Forecasting 48 hours ahead
After the training and tests performed for each model, the most consistent method to compare their efficiency is to compare to new and real data. A prediction for the next 48 hours was performed for LSTM Vanilla and Stacked and compared to real data (yellow) below:
LSTM Vanilla
- Average error: 2,04 ºC
- Maximum error: 4,44ºC
LSTM Stacked
- Average error: 1,36 ºC
- Maximum error: 3,16ºC
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.
In the 48-hour prediction, the Stacked LSTM model exhibited a reduced average absolute error and maximum temperature error compared to the Vanilla. These findings indicate that the inclusion of additional complexity in the model was advantageous for the LSTM architecture. The introduction of these additional layers allows the model to capture more complex patterns and dependencies in the data which can enhance its performance in prediction tasks. The observed decrease in errors suggests that the added complexity enabled the model to capture more of the underlying patterns and variability in the temperature data, resulting in more accurate predictions.
Both models exhibited a high degree of precision in predicting temperature values for the next 48 hours, with an average error rate of approximately 2% and 2ºC. However, a challenge encountered by both models was accurately identifying a secondary temperature valley that differed from the expected outcome. This issue highlights a potential limitation in the models’ ability to capture and interpret complex patterns in the temperature data, particularly in cases where unexpected fluctuations or anomalies occur. Further research may be necessary to improve the models’ sensitivity to such irregularities and enhance their overall performance for temperature prediction.
Thank you so much for reading! Any questions or suggestions please contact me via LinkedIn: https://www.linkedin.com/in/octavio-b-santiago/
If you want to implement this solution or learn more about LSTM algorithms you can find the full python code on my GitHub repo in the link below:
References
Datasource: https://mapas.inmet.gov.br/# — INMET (Instituto Nacional de Meteorologia) Public Dataset : CC0 1.0 Universal License (https://portal.inmet.gov.br/sobre)
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