Computational mindset is a website that covers technological and scientific topics on computation. The main focus of the website is on deep learning and neural networks. There is even a section that deals with quantum computing. Tensor Flow is the technology that is used for neural networks. This post aims to address the forecast of a univariate equally spaced time series using Tensor Flow. The article can be accessed here. The main idea behind this post is presented in the article along with the methods that are commonly used. By the end of the post, you will come to the conclusion. So, what are you waiting for? Let’s get started with the post.

Overview

The estimation of evenly distributed time series and a univariate using Tensor Flow through various network categorizations are dealt with in the post. Numerous network-style configurations such as ConvLSTM, Convolutional, Bidirectional LTSM, LSTM, and other cascading combinations can be evaluated by users with the help of the code. Thus, it will become possible for users to evaluate the functioning clearly on the command prompt of programs based on Python. This would allow them to implement the following characteristics.

● Generation of Dataset
● Network taxonomy description and configuration of hyper parameters
● The prognosis (Prediction)
● Generating of a dispersion graph that explicitly shows the findings
● Generation of a video involving the network learning process
● The Diagnosis

Python version 3 is used for listing the code. Keras is incorporated which tends to be embedded into Tensor Flow 2. It even consists of libraries of ImageIO, Pandas, MatPlotLib, and NumPy.

 

The Main Characteristics

The post discusses the following functions.

  1. Generation of Dataset

The following steps are used by the Python program uvests_gen.py to generate the datasets.

  • The generator function of the time series will be taken by the program in the syntax lambda body based on t (independent variable) in the command line.
  • The discretization step starts and ends with the independent variable period.
  • It constructs a dataset in CSV through the implementation of features from the previous interval.
  1. Network Taxonomy Description and Configuration of Hyper Parameters

The main purpose of the Python program fc_uvests_fit.py focuses on building a neural network continuously and performing its training based on the parameters. Once the training sequences has been created using uvests_gen.py, a model of the neural network can be built by passing the command line arguments for the taxonomy such as by passing the required types of layers like Dense, LTSM, and Conv, and so on.

  1. Prediction

Next, the step involves prediction. The Python program fc_uvests_predict.py deals with the determination of the forecast for the time series that has been learned during the training. After the model has been developed using fc_uvests_predict.py, the forecast would be calculated, and it would be compared with sample time series. This would allow the uncertainty value to be calculated between the series.

  1. Generating a Dispersion Graph for the Findings

The Python program fc_uvests_predict.py aims to view the test series, the forecast series, and the training series geographically. Finally, the forecast is determined once the model has been developed using fc_uvests_predict.py. To calculate the error value of the series, the expected prediction and the time series are contrasted.

  1. Generation of a Video Involving the Network Learning Process

The Python program fc_uvests_predict.py aims to create a video that shows the forecast about the training process and showcases the epochs changing. In order to create the video, the modelsnapfreq, modelsnapout, and the arguments have to be transferred using the fc_uvests_predict.py command.

The first thing that needs to be done is that the fc_uvests_predict.py function needs to be executed. It should apply the modelsnapfreq and modelsnapout parameters. The function will execute after mysnaps pass and five respectively. There will be an animated gif during the end “the animated gif file”. It will display a series of frames. Every frame will depict a forecast graph that would determine the model accessible towards nth interval.

 

What Methodologies Are Used?

There are four testing methodologies in the framework for conducting the diagnostics. They are mentioned below.

  • As you must be already aware, the first testing methodology is the written user feedback concerning the standard performance as well as generic error streams of different programs.
  • As for the second, it includes the video generation as mentioned earlier. The video will allow one to keep a track of how the neural net learns as each epoch changes.
  • Tensor Board is the third method that needs to be used. Logsout is the argument that is used as it defines the repository of the program where the log data is written of the Tensor Board analysis at the end and before the training process. Once again, the program fc_uvests_predict.py has to be run.
  • Finally, the fourth methodology that you need to know about is making the fc_uvests_predict.py function inspect the parameters through the specification of the metrics argument. The metric values which are measured at every interval would be displayed on the regular output and the loss function values. However, the loss function parameters and values would be stored in the CSV files in the folder where a directory path is specified as the dumpout argument. Now, what needs to be done is that the fc_uvests_predict.py program has to be run once again using the dumpout argument that has passed through the dump file. Then, the command nn_dumps_scatter.py has to be run.
  • Keep in mind that the images will be offered after each epoch change in the savefigdir directory. It will display a graph of metrics that had been selected along with the loss function.

 Conclusion

The forecast of time series using different neural network taxonomies is presented on the website through the implanting with Keras. The different combinations of networks are tested using the Python code for various neural network types such as ConvLSTM, Convolutional, and LSTM. The results of every function can be found on the website.