Time series
29 bites tagged Time series: interview questions with model answers, and 60-second explainers.
Describe two methods for generating prediction intervals or probabilistic forecasts
Tests uncertainty quantification for risk-adjusted decisions. Strong answers: (1) parametric intervals via forecast error variance and normal multipliers, (2) bootstrap residual resampling for empirical percentiles.
Random split vs walk-forward validation in forecasting
Random splits leak future data into training; walk-forward validation rolls the origin ahead, testing only on later observations.
Describe key components for EDA on three years of daily user sign-ups
This tests time-series decomposition intuition. A strong answer covers trend, seasonality, and noise via plots, autocorrelation, and calendar effects, plus checks for missing days and outliers. Red flag: jumping to forecast models before validating structure.
Determine if a 10% DAU drop is statistically significant
Tests signal vs noise in stable metrics. Good answers define a null hypothesis, compute a test statistic from historical variance, compare to a critical value at set alpha, and check seasonality. Red flag: calling a large drop real without baseline variance.
Transform a Time Series for a Gradient Boosting Model
Tests your ability to convert a sequential problem into a tabular one. A great answer covers creating lagged/rolling features and time-based features (e.g., day of week), and crucially, specifies a time-aware validation split.
Which model for forecasting with seasonality and trend?
This tests your knowledge of classical time series models. A good answer names Holt-Winters, explaining its level, trend, and seasonal components. It also discusses choosing between additive and multiplicative methods. A red flag is jumping to complex models.
Train-Test Split vs. Time-Series Cross-Validation
This tests your grasp of data leakage in temporal data. A good answer explains why random splits create lookahead bias, then details how rolling-origin validation respects time. A red flag is just describing methods without explaining *why* one is necessary.
Explain time series stationarity and how to achieve it
Tests your grasp of core time series assumptions. Define stationarity (constant mean/variance over time), explain why models need it for stable predictions, and name methods to test and achieve it. A red flag is just saying the data looks 'flat'.
How would you analyze a time series of user sign-ups?
This tests your structured approach to decomposing time series data. A strong answer identifies trend (long-term growth), seasonality (weekly/yearly patterns), and irregular components like spikes or dips.
Transform a time series for a supervised learning model?
This tests your ability to reframe a time series problem for tabular models. A great answer explains creating features from lags, rolling windows, and calendar data, then emphasizes using a time-aware validation split. A red flag is forgetting validation.
Forecasting inventory with trend and weekly seasonality?
This tests mapping a business problem to a statistical tool. A good answer names Holt-Winters, explains its level, trend, and seasonal components, and discusses additive vs. multiplicative seasonality.
Train-test split vs. time-series cross-validation?
Tests if you see why temporal data breaks random splits. Contrast random sampling with sequential 'walk-forward' validation, where you only use past data to predict the future.
Explain stationarity in a time series
This tests your grasp of core time series modeling assumptions. A strong answer defines stationarity (constant mean/variance), explains its importance for ARIMA (stable patterns), and names a test (ADF) and a fix (differencing).
Describe EDA for a 3-year daily user sign-up dataset.
Tests your structured approach to time series EDA. A good answer identifies trend, seasonality, and anomalies before modeling. A red flag is jumping to forecasting models or only mentioning the overall average growth, ignoring cyclical patterns.
Downsampling: Trading Granularity for Speed in Time Series Data
Downsampling trades resolution for speed by summarizing old, high-granularity metrics into coarser ones. This makes long-range queries faster and cheaper, common in systems like Thanos for long-term Prometheus data.
Causal Impact: Measuring Effects Without an A/B Test
Causal Impact estimates an intervention's effect by modeling a 'what if' counterfactual. It's used to measure lift from a new feature or ad campaign when a clean A/B test isn't possible.
Prophet: Time Series Forecasting for Seasonal Data
Prophet treats a time series as a sum of its parts: a long-term trend, seasonal cycles, and holidays. It's used for business forecasting, like predicting sales, when you have strong seasonal data. The footgun is using it for non-seasonal data.
Linear Regression: Forecasting with a Straight Line
Linear regression forecasts the future by drawing a straight line through past data. It's used to predict outcomes like sales based on ad spend or energy use based on temperature.
Downsampling: Trading Precision for Storage in Time Series Data
Downsampling trades precision for storage in aging time series data. It's like summarizing old notes: you keep key trends but discard granular details. This is vital for observability systems that need recent precision but only coarse historical views.
Time Series Database: A Logbook, Not a Filing Cabinet
A Time Series Database (TSDB) is a specialized logbook for data that happens over time, like server metrics or sensor readings. It's built for high-speed writes and fast range queries. The footgun: don't use it for relational data like user profiles.
Granger Causality: Forecasting, Not Causing
Granger Causality tests if one time series can forecast another, not if it causes it. It's used in econometrics to see if money supply changes predict inflation. The footgun is the name itself: it only shows predictive power, not true cause-and-effect.
ARIMA: Forecasting by Modeling Autocorrelation
ARIMA models forecast a time series by learning its "memory"—how past values influence the next. It's used for forecasting sales or server load where patterns are driven by internal dynamics.
Prophet: Automated Time Series Forecasting
Prophet treats forecasting like decomposing a signal, breaking a time series into trend, seasonality, and holiday effects. It excels at predicting business metrics with strong seasonal patterns, like web traffic.
Time Series Cross-Validation
Unlike standard cross-validation that shuffles data, time series cross-validation respects the arrow of time. It evaluates a forecasting model by training only on past data to predict a future point, then rolling forward. The footgun is using random k-folds.
Get Time series bites daily.
Five a day, five minutes, offline. With quizzes so it sticks.
The iPhone app is on the way
We are building it. Until it lands, nothing here is held back from you: every interview card, your saved cards, streaks and the job board all work in Safari, plus hundreds of free practice quizzes of thirty questions each. Sign in and it all carries over to the app the day it arrives.
Want it as an icon? Tap Share at the bottom of Safari, then Add to Home Screen. It opens full screen and the cards you have read stay available offline.