In the past couple of years, big companies hurried to publish their machine learning based time series libraries. This popularity shows that machine learning based time series prediction is in high demand. This article introduces the recently released Tensorflow time series library from Google. This library uses probabilistic models to describe time series.
This turns out to be a fascinating journey. The Tensorflow library models time series as a linear dynamical system with parameters. To learn the model parameters, it uses the Kalman filter algorithm and variational inference.
This article will explain why and how we use these techniques, and how they work together. I will introduce them as we go along. And I will show you that these techniques naturally occur to enable us to learn the model parameters.Time Series Data Encoding for Deep Learning, TensorFlow and Keras (10.1)
This article is about time series, so there will be subscripts. Local linear trend is the most basic model in Tensorflow time series. It has the following definition:. To model how the observations are generated by these equations, we use the following generative thinking:. They are the parameters of this model. You may ask, what kind of time series can a local linear trend model?
To get a concrete feeling of this model, I wrote this code to sample time series from it. The following figure shows 30 time series samples. I generated these curves by setting the standard deviations of all three noises to 1.
You can see some curves have clear up or down trend, while others have trends that change directions. As you can see, using different values for the model parameters allows you to model different time series. Now the question is, if I have a time series, say this one:. This curve is the skyline of the mountains in the featured image adapted from Pixabay by illustrator Joshua Jones.
But I want to have some fun to see how a local linear trend model tracks skyline from mountain to mountain.This is covered in two parts: first, you will forecast a univariate time series, then you will forecast a multivariate time series.
This tutorial uses a [weather time series dataset recorded by the Max Planck Institute for Biogeochemistry. This dataset contains 14 different features such as air temperature, atmospheric pressure, and humidity. These were collected every 10 minutes, beginning in For efficiency, you will use only the data collected between and As you can see above, an observation is recorded every 10 mintues.
This means that, for a single hour, you will have 6 observations. Similarly, a single day will contain 6x24 observations. Given a specific time, let's say you want to predict the temperature 6 hours in the future. In order to make this prediction, you choose to use 5 days of observations. Thus, you would create a window containing the last 5x observations to train the model.
Many such configurations are possible, making this dataset a good one to experiment with. The function below returns the above described windows of time for the model to train on. In both the following tutorials, the firstrows of the data will be the training dataset, and there remaining will be the validation dataset.
First, you will train a model using only a single feature temperatureand use it to make predictions for that value in the future. It is important to scale features before training a neural network. Standardization is a common way of doing this scaling by subtracting the mean and dividing by the standard deviation of each feature. You could also use a tf. Let's now create the data for the univariate model.
For part 1, the model will be given the last 20 recorded temperature observations, and needs to learn to predict the temperature at the next time step. Now that the data has been created, let's take a look at a single example. The information given to the network is given in blue, and it must predict the value at the red cross.
Before proceeding to train a model, let's first set a simple baseline. Given an input point, the baseline method looks at all the history and predicts the next point to be the average of the last 20 observations. RNNs process a time series step-by-step, maintaining an internal state summarizing the information they've seen so far. For more details, read the RNN tutorial. Let's now use tf. The following visualisation should help you understand how the data is represented after batching.
Let's train the model now. Due to the large size of the dataset, in the interest of saving time, each epoch will only run for steps, instead of the complete training data as normally done. This looks better than the baseline. Now that you have seen the basics, let's move on to part two, where you will work with a multivariate time series.
The original dataset contains fourteen features. For simplicity, this section considers only three of the original fourteen. The features used are air temperature, atmospheric pressure, and air density. As mentioned, the first step will be to standardize the dataset using the mean and standard deviation of the training data.Time-series data arise in many fields including finance, signal processing, speech recognition and medicine.
A standard approach to time-series problems usually requires manual engineering of features which can then be fed into a machine learning algorithm. For example, if one is dealing with signals i.
Time series forecasting
A similar situation arises in image classification, where manually engineered features obtained by applying a number of filters could be used in classification algorithms.
In this blog post, I will discuss the use of deep leaning methods to classify time-series data, without the need to manually engineer features. The dataset contains the raw time-series data, as well as a pre-processed one with engineered features. I will compare the performance of typical machine learning algorithms which use engineered features with two deep learning methods convolutional and recurrent neural networks and show that deep learning can approach the performance of the former.
I have used Tensorflow for the implementation and training of the models discussed in this post. In the discussion below, code snippets are provided to explain the implementation.
For the complete code, please see my Github repository. There are 9 channels in this case, which include 3 different acceleration measurements for each 3 coordinate axes. First, we construct placeholders for the inputs to our computational graph:. The convolutional layers are constructed using one-dimensional kernels that move through the sequence unlike images where 2d convolutions are used. These kernels act as filters which are being learned during training.
As in many CNN architectures, the deeper the layers get, the higher the number of filters become. Each convolution is followed by pooling layers to reduce the sequence length. Below is a simple picture of a possible CNN architecture that can be used:. The convolutional layers that are slightly deeper than the ones depicted above are implemented as follows:. Then, the classifier outputs logitswhich are used in two instances:.
The rest of the implementation is pretty typical, and involve feeding the graph with batches of training data and evaluating the performance on a validation set. Finally, the trained model is evaluated on the test set. LSTMs are quite popular in dealing with text based data, and has been quite successful in sentiment analysis, language translation and text generation. Since this problem also involves a sequence of similar sorts, an LSTM is a great candidate to be tried.
This size parameter is chosen to be larger than the number of channels. This is in a way similar to embedding layers in text applications where words are embedded as vectors from a given vocabulary. For the implementation, the placeholders are the same as above.
The below code snippet implements the LSTM layers:. There is an important technical detail in the above snippet. The rest is pretty standard for LSTM implementations, involving construction of layers including dropout for regularization and then an initial state. The next step is to implement the forward pass through the network and the cost function.
Here is what the code looks like. Notice that only the last last member of the sequence at the top of the LSTM outputs are used, since we are trying to predict one number per sequence the class probability.
The rest is similar to CNNs and we just need to feed the data into the graph to train. This is worse than the CNN result, but still quite good.
It is possible that better choices of these hyperparameters would lead to improved results. Previously, I have tested a few machine learning methods on this problem using the pre-engineered features. In this blog post, I have illustrated the use of CNNs and LSTMs for time-series classification and shown that a deep architecture can approach the performance of a model trained on pre-engineered features.
This is highly desirable, since one does not need to have domain expertise from where the data has originated from, to be able to train an accurate model. The sequence we used in this post was fairly small steps. One may wonder what would happen if the number of steps were much larger and worry about the trainability of these architectures I discussed.Editor's Note: This is the fourth installment in our blog series about deep learning. Read Part 1Part 2and Part 3. Time series analysis has significance in econometrics and financial analytics but can be utilized in any field, where understanding trends is important to decision making and reacting to changes in behavioral patterns.
For example, a MapR Data Platform customer, who is a major oil and gas provider, places sensors on wells, sending data to MapR Event Store that is then used for trend monitoring well conditions, such as volume and temperature. In finance, time series analytics is used for financial forecasting for stock prices, assets, and commodities. ARIMA models have been used for decades and are well understood.
However, with the rise of machine learning and, more recently, deep learning, other models are being explored and utilized, either to support ARIMA results or replace them.
Deep learning DL is a branch of machine learning based on a set of algorithms that attempts to model high-level abstractions in data by using artificial neural network ANN architectures composed of multiple non-linear transformations.
RNNs are a class of neural networks that depend on the sequential nature of their input. Such inputs could be text, speech, time series, and anything else in which the occurrence of an element in the sequence is dependent on the elements that appeared before it. Actually, it turns out that while neural networks are sometimes intimidating structures, the mechanism for making them work is surprisingly simple: stochastic gradient descent.
For each of the parameters in our network such as weights or biasesall we have to do is calculate the derivative of the parameter with respect to the loss, and nudge it a little bit in the opposite direction.
ANNs use a method known as backpropagation to tune and optimize the results. Backpropagation is a two-step process, where the inputs are fed into the neural network via forward propagation and multiplied with initially random weights and bias before they are transformed via an activation function. The depth of your neural network will depend on how many transformations your inputs should go through. Once the forward propagation is complete, the backpropagation step measures the error from your final output to the expected output by calculating the partial derivatives of the weights generating the error and adjusts them.
Once the weights are adjusted, the model will repeat the process of the forward and backpropagation steps to minimize the error rate until convergence. If you notice how the inputs are aligned in Fig. Recurrent Neural Networks RNNs are called recurrent because they perform the same computations for all elements in a sequence of inputs. RNNs are becoming very popular due to their wide utility.
They can analyze time series data, such as stock prices, and provide forecasts. In autonomous driving systems, they can anticipate car trajectories and help avoid accidents. They can take sentences, documents, or audio samples as input, making them extremely useful for natural language processing NLP systems, such as automatic translation, speech-to-text, or sentiment analysis.
For example, x1 could be the first price of a stock in time period one. St-1 is usually initialized to zero. For example, if we wanted to predict the next value in a sequence, it would be a vector of probabilities across our time series. RNN cells are developed on the notion that one input is dependent on the previous input by having a hidden state, or memory, that captures what has been seen so far. The value of the hidden state at any point in time is a function of the value of the hidden state at the previous time step and the value of the input at the current time step.
This example was done with a small MapR cluster of 3 nodes. This example will use the following:.GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together. If nothing happens, download GitHub Desktop and try again. If nothing happens, download Xcode and try again.
If nothing happens, download the GitHub extension for Visual Studio and try again. This tutorial is designed to easily learn TensorFlow for time series prediction. Each tutorial subject includes both code and notebook with descriptions. Skip to content. Dismiss Join GitHub today GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together.
Sign up. Jupyter Notebook Python. Jupyter Notebook Branch: master. Find file. Sign in Sign up. Go back. Launching Xcode If nothing happens, download Xcode and try again. Latest commit. Latest commit 95f6cef Aug 1, Use tf.
Now I am working on modifying previous source code for tensorflow ver. Notice I have received many request for revising the code for the current tensorflow version. I will provide summarized presentation file for the theory of time series prediction. And How to apply the tensorflow implementation for kaggle competitions.
Target implementation will be tensorflow v1. You signed in with another tab or window. Reload to refresh your session. You signed out in another tab or window. Add files via upload.Home About Contact Privacy Sitemap. Udemy - Tensorflow 2.
Time series classification with Tensorflow
What an exciting time. It's been nearly 4 years since Tensorflow was released, and the library has evolved to its official second version.
Tensorflow is Google's library for deep learning and artificial intelligence. Deep Learning has been responsible for some amazing achievements recently, such as:. Generating beautiful, photo-realistic images of people and things that never existed GANs. Speech recognition e. Siri and machine translation Natural Language Processing.
Even creating videos of people doing and saying things they never did DeepFakes - a potentially nefarious application of deep learning. Tensorflow is the world's most popular library for deep learning, and it's built by Google, whose parent Alphabet recently became the most cash-rich company in the world just a few days before I wrote this.
It is the library of choice for many companies doing AI and machine learning. In other words, if you want to do deep learning, you gotta know Tensorflow. This course is for beginner-level students all the way up to expert-level students. How can this be? If you've just taken my free Numpy prerequisite, then you know everything you need to jump right in. We will start with some very basic machine learning models and advance to state of the art concepts.
Along the way, you will learn about all of the major deep learning architectures, such as Deep Neural Networks, Convolutional Neural Networks image processingand Recurrent Neural Networks sequence data. Even if you've taken all of my previous courses already, you will still learn about how to convert your previous code so that it uses Tensorflow 2.
Sequences, Time Series and Prediction
This course is designed for students who want to learn fast, but there are also "in-depth" sections in case you want to dig a little deeper into the theory like what is a loss function, and what are the different types of gradient descent approaches. Deploying a model with Tensorflow Serving Tensorflow in the cloud. Deploying a model with Tensorflow Lite mobile and embedded applications. Instructor's Note: Since Tensorflow 2. Stay tuned!
Instructor's Note 2: This course focuses on breadth rather than depth, with less theory in favor of building more cool stuff. If you are looking for a more theory-dense course, this is not it. Generally, for each of these topics recommender systems, natural language processing, reinforcement learning, computer vision, GANs, etc.
I already have courses singularly focused on those topics. Next Post. Previous Post. Tidak ada komentar.This solution presents an example of using machine learning with financial time series on Google Cloud Platform.
Time series are an essential part of financial analysis. Today, you have more data at your disposal than ever, more sources of data, and more frequent delivery of that data.
New sources include new exchanges, social media outlets, and news sources. The frequency of delivery has increased from tens of messages per second 10 years ago, to hundreds of thousands of messages per second today. Naturally, more and different analysis techniques are being brought to bear as a result.
Most of the modern analysis techniques aren't different in the sense of being new, and they all have their basis in statistics, but their applicability has closely followed the amount of computing power available. The growth in available computing power is faster than the growth in time series volumes, so it is now possible to analyze large scale time series in ways that weren't previously practical. In particular, machine learning techniques, especially deep learning, hold great promise for time series analysis.
As time series become more dense and begin to overlap, machine learning offers a way to separate the signal from the noise. Deep learning holds potential because it is often the best fit for the seemingly random nature of financial time series. See the long version of the video. This solution uses public data from Quandl. Deploy and sign into Cloud Datalab. Follow the steps in the Cloud Datalab Quickstart. The tutorial runs in a Cloud Datalab notebook.
Cloud Datalab is built on Jupyter notebooks. The notebook is a pre-installed sample that is included in the Cloud Datalab distribution. To view the notebook:.
When you follow along with the tutorial, you can run individual cells as you go, or run all the cells first and then read through the tutorial. Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4. For details, see the Google Developers Site Policies. Why Google close Groundbreaking solutions. Transformative know-how. Whether your business is early in its journey or well on its way to digital transformation, Google Cloud's solutions and technologies help chart a path to success.
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