Deep Learning for Time Series Analysis: A Keras Tutorial Deep Learning for Time Series Analysis: A Keras Tutorial
Learn how to build a deep learning model for time series analysis with Keras 3.0. These step-by-step directions simplify the process... Deep Learning for Time Series Analysis: A Keras Tutorial

Learn how to build a deep learning model for time series analysis with Keras 3.0. These step-by-step directions simplify the process and guide you from beginning to end.

Building a deep learning model with Keras 3.0 is relatively straightforward. While data collection, model compilation, and training can be time-consuming, this open-source library simplifies the process — you only have to complete a few steps. 

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1. Keras Installation 

If you plan on building a deep learning model, Keras is an excellent library. It is straightforward to use and can streamline development — it can make even a complex time series analysis seem simple. 

You’ll need to install Keras if you don’t already have it. You must clone the repository to make a local copy using the “git clone https://github.com/keras-team/keras-core.git” command. 

To set up the necessary dependencies, use “pip install -r requirements.txt.” Use the command “python pip_build.py –install” to initiate installation. Once done, you must decide whether to use JAX, PyTorch, or TensorFlow. 

You need to install a backend framework — JAX, PyTorch, or TensorFlow — to use Keras. If you decide to use TensorFlow, you must install it first until version 2.16 is released. Otherwise, you’ll overwrite Keras. Use “import keras_core as keras” to verify your installation.

2. Data Collection 

You want to find a large source with enough training and testing instances. Typically, weather datasets are ideal because they follow a pattern, have few null values, and are widely available. However, you might be unable to find one.

In that case, consider using AI-generated synthetic datasets. It’s a sound strategy as it is constantly evolving, with the market anticipated to reach $1 345 billion by 2030. This way, you don’t have to worry about finding an intact, publicly available source. 

3. Data Visualization

The next step is to visualize a sample time series by plotting each feature — it reveals any anomalies in your data, helping you know what to address during preprocessing. The “plot_model()” function creates a plot of your model. 


4. Data Preprocessing 

A time series analysis is only useful if your information is intact and sanitized. You should standardize data formats and structures before moving on. This step is particularly crucial if you’re pulling from multiple sources.

Clean your data by excluding or replacing any null values. Alternatively, you can review your source to search for the missing information. You should also remove any outliers to prevent skewed output during training.

Next, you must normalize your data. Determine the range of minimum and maximum values you want to include. Once you apply your chosen normalization technique, replace your original features with the new values. 

5. Model Compilation 

Model complication configures your algorithm for the learning process. You must specify the loss function, optimization, and metrics before beginning training. 

The loss function compares your model’s actual output to the expected to identify deviation during the learning process. Optimization defines how it adjusts its parameters depending on the data it processes. Metrics evaluate its performance. 

Use the “model.compile()” command to leverage the model complication method Keras provides. Losses are available using “from keras import losses.” The “from keras import optimizers” and “from keras import metrics” work the same way.

If you set the loss function as mean absolute error, the optimizer as “sgd” and the metric as categorical accuracy, the sample code would look like “model.compile(loss = ‘mean_absolute_error’, optimizer = ‘sgd’, metrics = [metrics.categorical_accuracy]).”

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6. Model Training 

Once you’ve finished the model complication, it’s time to train. Use the “model.fit()” function to prepare your model for a fixed number of iterations. You set your input data, target data, batch size, cycles, and callbacks here. 

Use the “ModelCheckpoint callback to periodically save a model as you go. If the validation loss stops improving, you can use the “EarlyStopping” callback to interrupt training. These are likely the two most important ones you’ll use, but Keras has a full list if you want more options.

7. Model Evaluation

The “Model.evaluate()” function evaluates your model on your test data. It gives you the loss value for every batch. 

8. Model Prediction

The “Model.predict()” function gives predictions for all samples in a batch. Its result will be different from the “Model-evaluate()” function. 

Keep Testing Your Deep Learning Model 

Once you’re done building your model, you should keep testing it to maximize its accuracy. Consider using synthetic or existing datasets to make another if you want to grow more familiar with the development process.

April Miller

April Miller

April Miller is a staff writer at ReHack Magazine who specializes in AI, machine learning while writing on topics across the technology sphere. You can find her work on ReHack.com and by following ReHack's Twitter page.