Time-series-prediction | Time series deep learning models in TensorFlow-TFTS | Machine Learning library

by LongxingTan Python Version: v0.0.6 License: MIT

X-Ray Key Features Code Snippets Community Discussions(3)Vulnerabilities Install Support

kandi X-RAY | Time-series-prediction Summary

Time-series-prediction is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Tensorflow, Neural Network applications. Time-series-prediction has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can install using 'pip install Time-series-prediction' or download it from GitHub, PyPI.

This repository implements the common methods of time series prediction, especially deep learning methods in TensorFlow2. It's welcomed to contribute if you have any better idea, just create a PR. If any question, feel free to open an issue.

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Time-series-prediction has a low active ecosystem.

It has 684 star(s) with 150 fork(s). There are 22 watchers for this library.

There were 1 major release(s) in the last 12 months.

There are 7 open issues and 7 have been closed. On average issues are closed in 314 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of Time-series-prediction is v0.0.6

Quality

Time-series-prediction has 0 bugs and 0 code smells.

Security

Time-series-prediction has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

Time-series-prediction code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

Time-series-prediction is licensed under the MIT License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

Time-series-prediction releases are available to install and integrate.

Deployable package is available in PyPI.

Build file is available. You can build the component from source.

Installation instructions are not available. Examples and code snippets are available.

Time-series-prediction saves you 707 person hours of effort in developing the same functionality from scratch.

It has 2050 lines of code, 218 functions and 48 files.

It has low code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi has reviewed Time-series-prediction and discovered the below as its top functions. This is intended to give you an instant insight into Time-series-prediction implemented functionality, and help decide if they suit your requirements.

Calculate time delays
R Aggregate time delays
Splits the input tensor
Call the forward function
Perform the forward computation
Decodes the input x using the decoder
Run training
Plot the training history
Predict using the model
Detects the Mahala - Poisson distribution
Calculate Mahala - Poisson divergence
Train the model
Parse command line arguments
Set random seed

Get all kandi verified functions for this library.

Time-series-prediction Key Features

No Key Features are available at this moment for Time-series-prediction.

Time-series-prediction Examples and Code Snippets

No Code Snippets are available at this moment for Time-series-prediction.

Community Discussions

Trending Discussions on Time-series-prediction

High accuracy on LSTM-RNN model implemented on time-series forecasting

ValueError: Shape must be at least rank 3 but is rank 2 for '{{node BiasAdd}} = BiasAdd[T=DT_FLOAT, data_format="NCHW"](add, bias)' with input shapes:

Structure diagram of the keras LSTM

QUESTION

High accuracy on LSTM-RNN model implemented on time-series forecasting

Asked 2022-Jan-20 at 08:01

I am new in LSTM-RNN. I have tested many RNN-LSTM python code with .csv files for time-series. None of them had the accuracy that this guy here: https://machinelearningmastery.com/time-series-prediction-lstm-recurrent-neural-networks-python-keras/ has. How can he achieves that with just 4 LSTM cells?

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ANSWER

Answered 2022-Jan-20 at 08:01

I remember this article from years ago (it's from 2016). Don't expect too much from this. It's just a tutorial with toy data.

The author later half-acknowledged that data was too small, had bias and the model was greatly overfitted, which is easily spottable from the graph where predictions and ground truth are just lagging from each other. It's always a bad sign.

You can get that from the comments if you search for "bias, "lookahead" or "overfit".

Source https://stackoverflow.com/questions/70664959

QUESTION

ValueError: Shape must be at least rank 3 but is rank 2 for '{{node BiasAdd}} = BiasAdd[T=DT_FLOAT, data_format="NCHW"](add, bias)' with input shapes:

Asked 2021-Jun-22 at 15:36

Done

I am just trying to run and replicate the following project: https://machinelearningmastery.com/time-series-prediction-lstm-recurrent-neural-networks-python-keras/ . Basically until this point I have done everything as it is in the linked project but than I got the following issue:

My Own Dataset - I have tried with the dataframe:

I have tried with his original dataset fully 100% his code but I still have the same error
A.) having the 2 columns (1st column date and 2nd column target values),
B.) time code in to the index and dataframe only containing the target value.

INPUT CODE:

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ANSWER

Answered 2021-Jun-22 at 15:36

Solution

I switched to AWS EC2 SageMaker "Python [conda env:tensorflow2_p36] " so this is the exact pre made environment "tensorflow2_p36"
As I ahev read it in some palces it is probably library collision maybe with NumPy.

Source https://stackoverflow.com/questions/68036975

QUESTION

Structure diagram of the keras LSTM

Asked 2020-Nov-17 at 14:09

I was reading this post https://machinelearningmastery.com/time-series-prediction-lstm-recurrent-neural-networks-python-keras/ and i want draw in my mind the structure of the LSTM network. Analyzing this part of the code:

...

ANSWER

Answered 2020-Nov-17 at 14:09

No, you still have one LSTM layer with four LSTM Neurons.

BTW: If you're looking for a fast way to visualize an ANN: Netron

Source https://stackoverflow.com/questions/64876315

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install Time-series-prediction

You can install using 'pip install Time-series-prediction' or download it from GitHub, PyPI.
You can use Time-series-prediction like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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