pybats | Bayesian time series forecasting and decision analysis | Predictive Analytics library
kandi X-RAY | pybats Summary
kandi X-RAY | pybats Summary
PyBATS is a package for Bayesian time series modeling and forecasting. It is designed to enable both quick analyses and flexible options to customize the model form, prior, and forecast period. The core of the package is the class Dynamic Generalized Linear Model (dglm). The supported DGLMs are Poisson, Bernoulli, Normal (a DLM), and Binomial. These models are primarily based on Bayesian Forecasting and Dynamic Models.
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Top functions reviewed by kandi - BETA
- Calculate the LMM model
- R Define a density regression model
- Define Bayesian Probability Parameters
- R Define dglm model
- R Analyze the DBM model
- R Generate the DCM model
- R Define DCM
- Merge latent factor with predictor
- Return a shallow copy of the file
- Merge multiple latent factors
- Compute a zape - weighted forecast from a set of samples
- Calculate forecast for a given date
- Calculate marginal density for a Monte Carlo distribution
- Plot the coefficients in a matplotlib plot
- Interpolate a polynomial
- Plot a forecast
- Calculate the monthly forecast for a given day
- Calculate the median value of a given statistic
- Calculate the covariance coefficients for a given model
- Calculate the pct from a latent factor
- Calculate the hazard model
- R Analysis of DCMM
- Calculate joint marginal likelihood for a given DCMM
- Calculate the joint marginal likelihood of a joint marginal likelihood
- Compute linear interpolation
- Calculates the forecast of holiday effects
pybats Key Features
pybats Examples and Code Snippets
Community Discussions
Trending Discussions on Predictive Analytics
QUESTION
GPU is good for parallel computing but the problem is some machine learning libraries don't utilize the GPU, unless that machine learning based on image processing or some sort of graphics processing, what if I am using machine learning for predictive Analytics? do libraries like TensorFlow utilize the GPU? or they use only CPU? or can I choose which processing unit to use? whats the deal here?
note: predictive Analysis requires no graphics processing.
...ANSWER
Answered 2020-Nov-21 at 21:35The computation that happens in the GPU in any of the machine learning frameworks that support GPUs is not limited to graphical processing. For instance, if your model is a simple logistic regression, a framework such as TensorFlow will run it on the GPU if properly configured.
The advantage of GPUs for machine learning is that training big neural networks benefits greatly from the high level of parallelism that the GPUs offer.
If you want to know more about this, I'd recommend you start here or here.
some things to consider:- how much a model will benefit from running in the GPU will depend on how much it will benefit from parallel computation in general.
- Deep Learning models can be applied to predictive analytics, as well as more classical machine learning models. Bear in mind that neural nets are possibly the category of models that will benefit inherently from the GPU (see links above).
- Even though running models using GPUs (or even more specialised hardware) can bring benefits, I would suggest that you don't choose a framework and, especially, don't choose an algorithm based solely on the fact that it will benefit from parallelism, but rather look at how appropriate a given algorithm is for the data you have.
QUESTION
I have a pandas dataframe which is a large number of answers given by users in response to a survey and I need to re-structure it. There are up to 105 questions asked each year, but I only need maybe 20 of them.
The current structure is as below.
What I want to do is re-structure it so that the row values become column names and the answer given by the user is then the value in that column. In a picture (from Excel), what I want is the below (I know I'll need to re-name my columns, but that's fine once I can create the structure in the first place):
Is it possible to re-structure my dataframe this way? The outcome of this is to use some predictive analytics to predict a target variable, so I need to re-strcture before I can use Random Forest, kNN, and so on.
...ANSWER
Answered 2020-Nov-01 at 19:39You might want try pivoting your table:
QUESTION
I have js files Dashboard and Adverts. I managed to get Dashboard to list the information in one json file (advertisers), but when clicking on an advertiser I want it to navigate to a separate page that will display some data (Say title and text) from the second json file (productadverts). I can't get it to work. Below is the code for the Dashboard and next for Adverts. Then the json files
...ANSWER
Answered 2020-May-17 at 23:55The new object to get params in React Navigation 5 is:
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
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Install pybats
This is the most basic example of Bayesian time series analysis using PyBATS. We'll use a public dataset of the sales of a dietary weight control product, along with the advertising spend. First we load in the data, and take a quick look at the first couples of entries:.
Define the model (a Poisson DGLM)
Sequentially update the model coefficients with each new observation $y_t$ (also known as forward filtering)
Forecast $k=1$ step ahead at each desired time point
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