Answer
Use datetime indexing, resampling, rolling windows.
Model trends, seasonality.
Use pandas and statsmodels.
Model trends, seasonality.
Use pandas and statsmodels.
Quick Answer
Time series data handling: parse dates with pd.to_datetime(), set DatetimeIndex. Resample: df.resample("1H").mean() for hourly aggregation. Rolling statistics: df.rolling(7).mean() for 7-day moving average. Lag features: df["lag_1"] = df["value"].shift(1). Seasonality and trend decomposition: statsmodels.tsa.seasonal_decompose. Models: ARIMA, Prophet (Facebook), LSTM (deep learning).
S
SugharaIQ Editorial Team
Verified Answer
This answer has been peer-reviewed by industry experts holding senior engineering roles to ensure technical accuracy and relevance for modern interview standards.
Want to bookmark, take notes, or join discussions?
Sign in to access all features and personalize your learning experience.
Source: SugharaIQ