makeprediction 4.0.0

Automatic and fast Gaussian process for time serie prediction.

MakePrediction is a package for building an automatic Gaussian process regression (GPR) models for time series prediction in Python. It was originally created by [Hanany Tolba].

• MakePrediction is an open source project. If you have relevant skills and are interested in contributing then please do contact us ([e-mail]).*

Gaussian process regression (GPR):

The advantages of this Gaussian processes package:

• Very fast training.
• Very fast prediction.
• The prediction can be interpolated as desired.
• The training of the model is automatic, no kernel function needs to be specified. An optimal choice of kernel is automatically elaborated.
• Possibility to choose a kernel function manually.
• The prediction is probabilistic (Gaussian) so that confidence intervals can be calculated and used to decide whether to make a strategic decision.
• The package provides an API for deployment.

Where do you find time series?

• Energy
• Finance
• Medical, Biotech, and Healthcare
• IoT Monitoring
• Supply Chain
• Agriculture
• Retail

What does makeprediction do?

• Modelling and analysis time series.
• Automatic time-series prediction (forecasting).
• Real-Time time series prediction.
• Deploy on production the fitted (or saved) makeprediction model.

Applications:

• Energy consumption prediction.
• Energy demand prediction.
• Stock price prediction.
• Stock market prediction.
• ...

Latest release from PyPI

• pip install makeprediction

Latest source from GitHub

Be aware that the master branch may change regularly, and new commits may break your code.

MakePrediction GitHub repository, run:

• pip install .

Example

Here is a simple example:

import pandas as pd
import numpy as np

from makeprediction.gpts import GaussianProcessTimeSerie
from makeprediction.kernels import RBF, White
from makeprediction.visualization import Visualizer

#generate time series
###############################
  
x = pd.date_range(start = datetime.datetime(2021,1,1), periods=1000, freq = '3s' )
time2num = date2num(x)

# Simulate a data
date = pd.date_range(start = '2022',periods = 1000, freq = '30T')
# As sum of RBF kernel and Gaussian noise kernel
kernel = RBF() + White(variance = .01) 
# add mean and variance 
data = 1000 + 50*kernel.simulate(date, seed = np.random.seed(1234))
# create a dataframe with data
df = pd.DataFrame(data = data, index = date, columns=['value'])
df.head()


# split time serie into train and test
TRAIN_SIZE = int(.8*len(df))
df_train, df_test = df[:TRAIN_SIZE], df[TRAIN_SIZE:]

# Create an instance of the class GaussianProcessTimeSerie with train data:
#########################################
model = GaussianProcessTimeSerie(df_train.index, df_train.value)
# Show train data with test data 
Visualizer.iplot(model, df_test.index, df_test.value)

# fit the model
model.fit()

#predict with model and plot result
model.predict(df_test.index)
Visualizer.iplot(model, df_test.index, df_test.value)

# Show initial score 
model.score(df_test.value)

# Online prediction with updating 
ypred = np.empty(shape = (0,))
ypred_std = np.empty(shape = (0,))

for x,y in df_test.itertuples():
    # predict for x value
    yp,yp_std = model.predict(x,return_value = True)
    ypred = np.append(ypred,yp)
    ypred_std = np.append(ypred_std,yp_std)
    # update the model for (x,y)
    data = {'x_update': x, 'y_update': y}
    model.update(**data)
#Set all new prediction to the model    
model.set_prediction(df_test.index, ypred, ypred_std)

# Show new prediction 

Visualizer.iplot(model, df_test.index, df_test.value)

The previous prediction with updating, can be obtained simply by the "predict" method as follows:

# Show new score 
model.score(df_test.value)

{'TrainErrors': {'MBE': 1.9652784019209927e-05,
  'MAE': 3.8085808450980743,
  'MSE': 22.963876468774956,
  'RMSE': 4.792063904913514,
  'NRMSE': 0.004642410409421008,
  'R2': 0.9867205336559287},
 'TestErrors': {'MBE': -0.3680353774898197,
  'MAE': 4.749355093190354,
  'MSE': 34.36287534118008,
  'RMSE': 5.8619856142078755,
  'NRMSE': 0.005905883435157759,
  'R2': 0.9879230611904987}}