You're on the right track by diving into time series forecasting in machine learning! Since you're already exploring the basics and popular forecasting methods, here are some scientifically grounded suggestions to deepen your understanding and enhance the quality of your analyses:

1. Go Beyond Traditional Forecasting Methods

While classical models like ARIMA, Exponential Smoothing (ETS), and Holt-Winters are widely used, machine learning-based approaches such as Long Short-Term Memory (LSTM), Transformer-based models, and Temporal Fusion Transformers (TFT) have revolutionized time series forecasting. Consider comparing these approaches on the same dataset to evaluate their strengths and weaknesses.

2. Feature Engineering is Key

Unlike traditional statistical models, machine learning approaches benefit significantly from feature engineering. Some useful engineered features include:

Lag variables (previous values of the series)

Rolling statistics (moving averages, standard deviations)

Fourier transformations (to capture seasonality)

External covariates (macroeconomic indicators, weather data)

3. Consider the Stationarity of Your Data

Many traditional forecasting models assume stationarity (constant mean and variance over time). If your data is non-stationary:

Use differencing or log transformations to stabilize variance.

Test for stationarity using the Augmented Dickey-Fuller (ADF) test.

If using ML models, consider models like XGBoost or LSTMs, which can work well with non-stationary data.

4. Model Selection and Hyperparameter Tuning

Each forecasting method has hyperparameters that significantly impact accuracy:

For ARIMA: Use grid search over (p, d, q) to find optimal values.

For ML models: Tune parameters like number of trees (Random Forest, XGBoost) or number of layers and neurons (LSTM, Transformers).

5. Performance Metrics Matter

Evaluate models using the right performance metrics:

Mean Absolute Percentage Error (MAPE): Good for percentage-based accuracy.

Root Mean Squared Error (RMSE): Penalizes larger errors more heavily.

Mean Absolute Error (MAE): A simpler alternative to RMSE.

Diebold-Mariano Test: Use this test to compare two forecasting models statistically.

6. Understand Real-World Constraints

In real-world applications:

Data availability and quality can be a bottleneck.

Concept drift (changes in underlying patterns) can reduce model accuracy over time.

Computational efficiency matters, especially for real-time forecasting (e.g., in financial markets).

7. Experiment with Advanced Methods

Try Facebook’s Prophet if you want an interpretable ML-based model.

Experiment with Attention-based Transformers (e.g., Informer, Time Series Transformers) for long-range dependencies.

Use AutoML frameworks (like AutoTS, H2O AutoML) for automated feature selection and model tuning.

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Final Advice

If you're making educational content on this topic, consider: ✔ Comparing classical vs. ML-based methods on the same dataset.

✔ Demonstrating real-world applications (e.g., stock price forecasting, sales predictions).

✔ Explaining trade-offs (e.g., accuracy vs. interpretability).

By integrating statistical rigor, ML advancements, and real-world applications, you’ll not only master time series forecasting but also create valuable educational content for others.