Questions:

1. Can we skip the step of testing the differencing result?

2. If YES, when you can skip testing? (give situation)

3. If NO, why skipping testing is risky?

Your answer:

1. ......

2. ......

3. ......

1. No, we should not skip the step of testing the differencing result.

2. Even if we apply differencing, we can’t be sure the series has become stationary without proper testing like the ADF (Augmented Dickey-Fuller) test.

3. Skipping this step is risky because if the data is still non-stationary, our time-series model (like ARIMA) will produce misleading or invalid forecasts. Testing ensures the assumptions are satisfied before moving forward.

• ARIMA, SARIMA or other linear models require stationarity. Failing to test could lead to poor forecasts or model diagnostics.

In the case of SHOULD skip the differencing:

• Models used (eg. XGBoost, LSTM, Prophet) don't assume stationarity. These models can capture trends and seasonality directly from the data without requiring differencing or stationarity.

• Ensure Stationarity before performing time series model that needs differencing like ARIMA

• Confirm Model Accuracy by removing trends

Yes

• Can skip during the EDA when visualizating to understand data

• Pre validated data that has been ensured data is stationary

Yes, if using built in library like auto_arima.

1. No, usually you should not skip it.

2. You can only skip it if you made the data yourself and know exactly how it works, or in very rare, simple cases. Not for real-world data.

3. It's risky because your data might still be unstable (not "stationary"), or you might change it too much. This makes your models wrong and your predictions bad.

1. No, cannot be skipped. Due to tend to have poorer model performance. If the series is still non stationary after differencing, the ARIMA model will provide unreliable outcomes.

2. Can only be skipped when the data is stationary but it is risky to do so.

1. Usually, it is not recommended to skip. The differencing result may hide problems such as calculation errors and data mismatches. Testing is a key step to ensure the reliability of the result.

2. When the differencing process is simple, verified error - free (e.g., fixed - value subtraction with unchanged data). Still, testing is recommended.

3. Wrong results enter subsequent steps, causing analysis and decision - making errors, damaging project reliability.

1. Yes, but only in specific situations where testing is not critical or necessary.

2. If Yes,

• When the data is already known to be stationary.

• When using models that don’t require stationarity (e.g., LSTM, Prophet).

• During quick exploratory analysis where precision isn’t crucial.

3. If No,

• It may lead to using incorrect model parameters (like wrong differencing order).

• Over-differencing can remove useful information.

• Non-stationary data in models like ARIMA leads to poor forecasts and unreliable results.

2.You may skip testing only in exploratory or prototyping stages, or if the data is already known to be stationary after differencing (e.g., simulated data).

3.Skipping can lead to under- or over-differencing, incorrect model assumptions, and poor forecast accuracy. Testing ensures the data is truly stationary for reliable modeling.

Answer:

1. Generally NO

2. If yes, it might probably the data is already been verified or prepared to be stationary. Or else, it is synthetic or simulated data.

3. If no, it will lead to lose the valuable information and adding unnecessary noise. It is high risk of using an invalid model if skipping the step of testing.

1. No, we should not skip testing the differencing result because it is essential to confirm whether the data has become stationary after differencing.

2. Yes, in certain automated modeling situations like using auto_arima() in Python or when the model internally manages differencing, you might skip manual testing — but only with proper caution and validation.

3. Skipping testing is risky because it may lead to incorrect assumptions about stationarity, cause over- or under-differencing, and result in poor model performance, inaccurate forecasts, or misleading conclusions.

Yes, if the model is inherently stationary.

Yes. for some models that accept non stationary like prophet model. this models able to handle non stationary data.

2. Yes, if the business context indicates that the data is already stationary (such as certain sensor readings), or if use models that do not strictly require stationarity, can skip the test.