Issue #11: The ultimate ARIMA cheat sheet for Time Series Forecasting

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💊 Article of the week

We present to you the ultimate cheat sheet on using ARIMA models for time series forecasting with Python. This comprehensive guide is perfect for anyone looking to understand and implement ARIMA models for predicting future trends and making informed decisions.

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✍️ Test your knowledge!

1. What are the ACF and PACF plots for?

2. If you are not using autoARIMA, how would you determine the optimal parameters?

3. What do you need to consider when using exogenous variables in ARIMA?

The correct answers will be revealed next week!

📢 What’s everyone talking about?

This week we will share some interesting AI-related news:

• Auto-GPT is a GPT-4 model that can write blog posts, conduct market research, schedule Instagram posts, and respond to customer queries on its own without requiring manual input.

• It breaks down its steps into "thoughts," "reasoning," and "criticism" to generate high-quality text. It can accomplish user-defined goals autonomously and has features such as long/short-term memory and text-to-speech integration. These features make Auto-GPT feel more human-like and easier to interact with.

• While this technology is impressive, it raises concerns about job losses and the unchecked progress of AI, prompting caution and calls for safety protocols from experts like Elon Musk and Jaan Tallinn.

💡 We also recommend…

• Discover the three main Python libraries: NumPy, pandas and matplotlib.

• Extend your knowledge about ARIMA with our articles on the topic:

  • Introduction to ARIMA

  • Parameters selection in ARIMA

  • Seasonal ARIMA

❓ Get ready for your interview!

What could you do if your model performs well on a training set but not so well on live data?

1. Check for overfitting: One of the most common reasons why a model might perform well on training data but not on live data is overfitting. Overfitting occurs when the model learns the training data too well and is unable to generalize to new data. To check for overfitting, look at the model's performance on a holdout set, which is a set of data that is not used for training. If the model's performance on the holdout set is significantly worse than its performance on the training set, then it is likely that the model is overfitting.

2. Check for data leakage: Another common reason why a model might perform well on training data but not on live data is data leakage. Data leakage occurs when the model is trained on data that includes information about the test data. Although your model may perform well on the test data, it is possible that it will not generalize as well to live data because of data leakage. To check for it, look at the data that was used to train the model and make sure that it does not include any information about the test data.

3. Check for model selection bias: Model selection bias occurs when the model is selected based on its performance on the training data. This can lead to the model being overfit to the training data and not generalizing well to new data. To avoid model selection bias, use a cross-validation technique to select the model. Cross-validation involves splitting the data into multiple subsets. The model is then trained on one subset and evaluated on another subset. This process is repeated multiple times, and the model that performs best on average is selected.

4. Check for model complexity: The complexity of a model can also affect its performance. A model that is too complex might overfit the training data and not generalize well to new data. A model that is too simple might not be able to capture the complex relationships in the data. To find the right balance of complexity, experiment with different models and evaluate their performance on the training and test data.

5. Check for data quality: The quality of the data can also affect the performance of the model. If the data is noisy or incomplete, it can make it difficult for the model to learn the correct relationships. To improve the quality of the data, clean it and remove any errors or inconsistencies. In addition, try to collect more data if possible.

Don't miss out on additional questions on the website!

📝Check if you were right!

1. What is NumPy and what are its features?

   NumPy is a Python library for numerical computing. Its features include support for large, multi-dimensional arrays and matrices, and a wide range of mathematical functions to operate on these arrays.

2. How do you initialize a NumPy array?

   NumPy arrays can be initialized using functions such as np.array(), np.zeros(), np.ones(), np.arange(), and np.linspace().

3. What are the differences between Python arrays and NumPy arrays?

   While Python arrays are a basic built-in data type that can store homogeneous elements, NumPy arrays are a more powerful data structure that can handle multidimensional arrays of homogeneous or heterogeneous data types with built-in mathematical functions optimized for numerical operations.

You can read more about this in our previous article: Introduction to NumPy.