Time series data is everywhere. But what is it? And how can you harness it with deep learning? This tutorial will show you everything you need to know.

Check out this video for more information:

## Introduction

In this tutorial, you will discover how you can develop an LSTM model for multivariate time series forecasting.

After completing this tutorial, you will know:

– How to identify the problem as one that is suitable for deep learning.

– How to develop a Santa Barbara Policing Department crime report LSTM model.

– How to make predictions with your model.

## Why Time Series?

In the past, much of the focus in machine learning has been on developing algorithms that can learn from static data. However, there is an growing interest in developing algorithms that can learn from time series data. Time series data is data that is collected over a period of time, such as stock prices or weather data. There are many reasons why deep learning is well suited for time series data.

First, deep learning algorithms are able to learn complex relationships between input and output variables. This is important for time series data because there can be many complex relationships that exist between input and output variables over time. For example, the relationship between stock prices and company earnings may be different in the short-term vs. the long-term. Deep learning algorithms are able to learn these complex relationships so that they can make better predictions about future values.

Second, deep learning algorithms are able to handle nonlinearities well. Time series data often contains nonlinearities, such as seasonality or trends. Deep learning algorithms are able to learn these nonlinearities so that they can make better predictions about future values.

Third, deep learning algorithms are able to handlemissing values well. Time series data often contains missing values, such as when data is collected at irregular intervals or when there are gaps in the data (such as during weekends or holidays). Deep learning algorithms are able to learn from data with missing values so that they can make better predictions about future values.

Fourth, deep learning algorithms are efficient at handling large amounts of data. Time series data often contains a lot of information, which can be difficult for traditional machine learning algorithms to handle efficiently. Deep learning algorithms are designed to handle large amounts of data effectively so that they can make better predictions about future values.

Finally, deep learning algorithms have been shown to be effective at making predictions about time series data. In general, deep learning outperforms traditional machine learning techniques when it comes to making predictions about time series data (see here and here for some examples).

## Data Preprocessing

In this tutorial, we will cover the important topic of data preprocessing for time series data. Time series data is data that is ordered in time, such as daily stock prices or monthly weather measurements. Because time series data is ordered, it can be very challenging to train deep learning models on this type of data. In this tutorial, we will show you how to perform a number of important preprocessing steps for time series data, including scaling, outliers detection, and trend and seasonality removal. After completing this tutorial, you will be able to apply these techniques to your own time series datasets.

## Building the Model

In this section, we will build the deep learning model for our time series data. We will be using the Keras library for this tutorial. Keras is a high-levelAPI that makes it easy to build deep learning models.

## Training the Model

In this section, we will train our time series deep learning model. We will first split our dataset into a training and test set, and then train our model on the training set. Finally, we will evaluate our model on the test set.

To split our dataset into a training and test set, we will use the train_test_split() function from the scikit-learn library. We will also specify a test_size of 0.2, which means that 20% of our data will be used for testing.

After we have split our data, we will train our model on the training set. We will use the fit() function from the Keras library to do this.

Finally, we will evaluate our model on the test set. We will use the evaluate() function from Keras for this.

## Evaluating the Model

After you have trained your model, it is important to evaluate its performance on unseen data. This will give you an idea of how well the model generalizes to new data and whether or not it is overfitting.

There are two main ways to evaluate a time series model: holdout validation and cross-validation.

In holdout validation, you split the data into a training set and a test set. The model is trained on the training set and then evaluated on the test set. This is a simple and straightforward way to evaluate the model, but it can be prone to overfitting if the training and test sets are not random.

Cross-validation is a more robust way to evaluate the model. In cross-validation, you split the data into k partitions (usually k=10). The model is then trained on k-1 partitions and evaluated on the remaining partition. This process is repeated k times so that each partition is used as the test set once. The final results are then averaged over all k runs.

Cross-validation is more robust than holdout validation because it reduces the chances of overfitting. However, it is also more computationally expensive because the model has to be trained multiple times.

## Conclusion

We hope you enjoyed this tutorial! Time series deep learning is a powerful tool that can be used to model and predict complex data. With the right dataset, it can be used to achieve great results. We encourage you to experiment with different architectures and datasets to see what works best for you.

## Further Reading

If you want to learn more about time series deep learning, there are a few great resources that we recommend.

The first is Deep Learning for Time Series Forecasting by Jason Brownlee. This book provides a great overview of the topic and includes several practical examples.

The second resource is the Time Series Deep Learning repository on Github, which includes several Jupyter notebooks with code examples.

Finally, if you want to keep up with the latest advancements in this field, we recommend following the Time Series Deep Learning Blog.

## References

Time Series Deep Learning, from Scratch

1. Avila, L., & Soares, B. (2016). Attention-based neural networks for joint intent detection and slot filling in Dialog Systems. Pattern Recognition Letters, 80, 30-35.

2. Bengio, Yoshua, et al. “Learning long-term dependencies with gradient descent is difficult.” International conference on artificial neural networks. Springer, Berlin, Heidelberg, 1994.

3. Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural computation, 9(8), 1735-1780.

4.Rumelhart, D., Hinton, G., & Williams, R.(1986). Learning internal representations by error propagation . In Parallel Distributed Processing: Explorations in the Microstructure of Cognition (Vol 1 , pp 318-362). MIT press

## About the Author

Hi, I’m Jason Brownlee. I’m a machine learning engineer and researcher. I have a PhD in Artificial Intelligence and several years of experience working on machine learning projects. I created this tutorial to help people get started with deep learning for time series analysis.

Keyword: Time Series Deep Learning Tutorial