Goal: Understand algorithms deeply (pattern detection, time-series analysis, anomaly detection). Not just run code — know why and when each method applies.
Before you start: Read EXPLORATION-PRINCIPLES.md. Do not jump to coding; understand pattern → algorithm → result → explanation. Relate every concept to JumpIQ dealership data.
Deliverable (Daily): Daily progress report (what you studied + how it applies to dealership data). Deliverable (Friday Demo): Concept explanation (Trend, Z-Score, Seasonality), implementation approach (Agents), and a working Python PoC script.
Concepts:
- Slope calculation (e.g. linear regression on time index)
- Moving average (smoothing, then direction)
- Comparing periods (e.g. last 3 months vs previous 3 months)
Practice:
- Implement or use slope on a simple time series.
- Compare moving average vs raw series.
How this applies to JumpIQ: Revenue and valuation are time-based. Trend (slope, moving average) answers: Is this dealership’s revenue/valuation going up or down over time? Needed for quadrant classification (Champions vs Stragglers) and opportunity detection.
Concepts:
- Seasonal decomposition (trend + seasonal + residual)
- Prophet (additive seasonality, holidays)
- ARIMA (including seasonal ARIMA — SARIMA)
Practice:
- Decompose a series with clear seasonality (e.g. monthly car sales).
- Use Prophet or SARIMA on the same series; interpret components.
How this applies to JumpIQ: Dealership sales spike in Oct–Nov (festivals). Seasonality detection tells us is this spike normal for this time of year? so we don’t wrongly flag a seasonal bump as an anomaly or opportunity.
Concepts:
- Z-score:
(x - mean) / std; flag if |z| > 2 or 3. - IQR: Q1, Q3, IQR = Q3−Q1; flag if x < Q1−1.5×IQR or x > Q3+1.5×IQR.
- Isolation Forest: sklearn
IsolationForest— understand what it does and how to interpret.
Practice:
- Add an artificial outlier to a series; detect it with Z-score and IQR.
- Compare with Isolation Forest; note interpretability trade-off.
How this applies to JumpIQ: Sudden drop (e.g. 100 cars → 2 cars) could be data bug, API issue, or real problem. Outlier detection triggers investigation; Z-score/IQR are interpretable (“this point is 4 std away from mean”).
Concepts:
- ARIMA: AutoRegressive Integrated Moving Average (order p, d, q).
- Prophet: Trend + seasonality + holidays; good for business series.
- Simple baselines: last value, moving average.
Practice:
- Fit ARIMA and Prophet on a small dataset.
- Compare forecasts and interpret (trend vs seasonal).
How this applies to JumpIQ: Forecasting supports “what happens next?” for revenue/valuation and helps set expectations. When actual deviates from forecast, that can trigger anomaly investigation. Use interpretable methods so we can explain.
- Trend (slope, moving average) — simplest.
- Outliers (Z-score, IQR) — no forecasting needed.
- Seasonality (decomposition, then Prophet/ARIMA).
- Forecasting (ARIMA, Prophet) — builds on trend + seasonality.
- Python
- Pandas — series, dates, grouping
- NumPy — basic stats (mean, std, percentiles)
- statsmodels — decomposition, ARIMA
- prophet (optional) — Prophet
- scikit-learn — Isolation Forest, regression
Your senior asked for daily reports that prove real understanding. Include:
- Today I studied: e.g. trend detection, slope calculation, moving average.
- How it applies to dealership revenue: one or two sentences tying the concept to JumpIQ (e.g. “Slope tells us if revenue is improving or declining over time; we need this for Champions vs Stragglers.”).
- Tomorrow I will: one concrete next step.
Keep it short and consistent; focus on depth of understanding, not quantity. Do not report “I coded X” without “I understand how/why/when it works.”