Predict Time Series Data using GMDH Method in Python in 2 minutes

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To implement GMDH in Python, we will use the PyGMDH package. This package provides an easy-to-use interface for building and training GMDH models. Let's walk through the steps to predict time series data using the GMDH method in Python:

Step 1: Importing the Required Libraries

To begin, we need to import the required libraries for our analysis. We will use NumPy, Pandas, and PyGMDH for this tutorial.

import numpy as np
import pandas as pd
from pygmdh import GMDHRegressor        

Step 2: Loading the Dataset

We will be using the stock price dataset for this tutorial. Let's load the dataset into a Pandas DataFrame.

df = pd.read_csv('stock_prices.csv')        

Step 3: Preprocessing the Data

Before we can apply the GMDH algorithm to our dataset, we need to preprocess the data. We will split the data into training and testing sets, and also normalize the data using the MinMaxScaler.

from sklearn.preprocessing import MinMaxScaler
scaler = MinMaxScaler()
df['Price'] = scaler.fit_transform(df['Price'].values.reshape(-1,1))

# Splitting the data into training and testing sets
train_data = df.iloc[:45]
test_data = df.iloc[45:]        

Step 4: Building and Training the GMDH Model

Now that we have preprocessed the data, we can build and train our GMDH model. We will use the GMDHRegressor class from the PyGMDH package for this task.

# Creating the GMDH model
model = GMDHRegressor()

# Training the model
X_train = train_data.index.values.reshape(-1,1)
y_train = train_data['Price'].values.reshape(-1,1)
model.fit(X_train, y_train)        

Step 5: Making Predictions on the Test Data

Once we have trained our model, we can use it to make predictions on the test data.

# Making predictions on the test data
X_test = test_data.index.values.reshape(-1,1)
y_test = test_data['Price'].values.reshape(-1,1)
predictions = model.predict(X_test)

# Scaling the predictions back to the original values
predictions = scaler.inverse_transform(predictions)        

Step 6: Evaluating the Model Performance

Finally, we can evaluate the performance of our model using various metrics such as mean squared error (MSE) and mean absolute error (MAE).

from sklearn.metrics import mean_squared_error, mean_absolute_error
mse = mean_squared_error(test_data['Price'], predictions)
mae = mean_absolute_error(test_data['Price'], predictions)
print('Mean Squared Error:', mse)
print('Mean Absolute Error:', mae)        

Thats it. It's done. Congrats.

Now little background about python and GMDH.

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Predicting time series data can be a daunting task, particularly because there are often many variables involved and the data can be very complex. However, with the right tools and techniques, it is possible to accurately forecast future trends and make informed decisions based on that information. In this article, we will explore one such technique – the Group Method of Data Handling (GMDH) method – which can help you to easily predict time series data using Python.

GMDH is a powerful and versatile method for time series forecasting, and it is particularly useful when dealing with large and complex data sets. By leveraging the power of machine learning and artificial intelligence, GMDH can quickly and accurately identify patterns and trends in your data, allowing you to make informed decisions about the future. Whether you are a data scientist, a business analyst, or just someone interested in exploring the possibilities of time series forecasting, this article will provide you with all the information you need to get started with GMDH in Python.

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