基于R的时间序列预测：训练测试集划分策略合理性与无拆分模型评估方法问询

First, a quick critical clarification: Forecasting refers specifically to predicting future points in a time-ordered dataset, while Prediction is a broader term for any type of predictive modeling (classification/regression) where time sequence might not be a factor. This distinction is key to understanding the workflows you're asking about.

Let's dive into your questions one by one:

1. After selecting the best model via train/test split, should we use the full dataset y for future forecasting? What's the evaluation logic here? #

Absolutely—this is the standard, recommended approach, and here's why:

• When you split your data into training_y and test, you're performing out-of-sample validation. This simulates how the model would perform on "unseen" data (exactly what future data will be). By comparing accuracy metrics (from accuracy(my_predictions, test)) across models, you're selecting the one that generalizes best to new data, not just the one that overfits the training set.
• Once you've identified the best model architecture (like the ARIMA parameters chosen by auto.arima()), retraining on the full y dataset lets you leverage every available data point to capture the underlying time-series pattern. More data means a more robust model that better captures trends, seasonality, and noise.
• The evaluation logic balances two goals: first, validate that the model doesn't overfit using the test set, then use all available data to build the most accurate possible forecasting model.

2. Is the workflow of first selecting a model via train/test split, then retraining on the full dataset for forecasting reasonable? #

This isn't just reasonable—it's the industry standard best practice for time-series forecasting. Let's break down why it's better than skipping validation:

• If you skip the train/test split and train directly on y, you have no way to check for overfitting. For example, auto.arima() might pick a complex model that fits historical data perfectly but fails on future data because it learned noise instead of the true underlying pattern.
• The train/test split acts as a "reality check": it tells you how much you can trust the model's future predictions. Once you confirm a model performs well on the test set, retraining on the full dataset gives you the most accurate forecast possible—you're not wasting any data that could help the model learn the time-series behavior.
• Think of it like studying for an exam: you practice with past papers (the test set) to figure out what works, then review all your notes (the full dataset) before taking the real exam (forecasting future data).

3. What's the evaluation method for forecasting without train/test split? #

If you skip the train/test split, you're limited to in-sample evaluation—a far less reliable approach:

• You can calculate metrics like RMSE or MAE by comparing the model's fitted values (predictions it makes for the historical data it was trained on) to the actual historical data. For your ARIMA model, this would look like accuracy(my_model, y).
• The major issue here is that in-sample metrics almost always look better than real-world performance. Models are designed to fit the data they're trained on, so they'll naturally perform well on that data—but this tells you nothing about how they'll handle unseen future points.
• Without a test set, you're essentially guessing that your model will generalize. There's no way to quantify its expected error on future data, making this approach risky for any real-world application where forecast accuracy matters.

内容的提问来源于stack exchange，提问作者binaryskull