Can Machine Learning Outperform Statistical Models for Time Series Forecasting? - Satyajit Chaudhuri su Medium

People often pick standard models like ARIMA (Auto Regressive Integrated Moving Average), Exponential Smoothing, and Seasonal Decomposition of Time Series (STL) for time series analysis. They’re good because they’re easy to understand, sturdy against data assumptions, and based on strong theory. This is why they’re top choices for future predictions.

As machine learning advances, we see a big increase in their use for predicting time series data. Tools like bagging and boosting stand out because they show great ability to spot tricky patterns and unusual links in time series data. Their unique feature is learning directly from raw data. They can deal with massive amounts of data and make accurate guesses.

The following article is an experimental study that uses both statistical forecasts and machine learning-based forecasts to predict the future for a practical use case. The study then proceeds to compare the forecasts based on certain accuracy metrics in order to judge if the machine learning models can give competition to their traditional counterparts.

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