| # | Math Trick | Description | Use Case Example |
|---|---|---|---|
| 1 | Moving Averages | Smooths data using a rolling average (SMA, WMA, EMA) | Forecasting trends in sales or traffic |
| 2 | Exponential Smoothing | Weights recent data more for responsive short-term forecasting | Predicting next day's value |
| 3 | Linear Regression | Fits a straight trend line to data | Predicting future sales or prices |
| 4 | Polynomial Regression | Fits a curve to account for nonlinear trends | Modeling growth with acceleration |
| 5 | Logarithmic/Exponential Trend | Fits curved models like exponential or logarithmic | Forecasting growth or decay |
| 6 | Percent Change / Growth Rate | Uses % increase/decrease to project future values | Estimating next month's revenue |
| 7 | Seasonal Averaging | Averages data by time periods (month, week, etc.) | Forecasting monthly sales |
| 8 | Rolling Regression | Applies linear regression over a sliding window | Short-term stock price prediction |
| 9 | Z-Score Anomaly Detection | Identifies and removes outliers using standard deviations | Cleaning noisy time series |
| 10 | Fourier Transform (FFT) | Finds cyclical patterns via frequency analysis | Detecting seasonality in demand patterns |
why not have another 10?
| # | Math Trick | Description | Use Case Example |
|---|---|---|---|
| 11 | CAGR (Compound Annual Growth Rate) | Measures average annual growth rate over time | Forecasting investment growth |
| 12 | Autoregressive (AR) Models | Uses past values to predict the next one | Time series forecasting like AR(1), AR(2) |
| 13 | Differencing (Δ) | Subtracts previous values to remove trend or seasonality | Stationarizing a time series |
| 14 | Cumulative Sum (CUSUM) | Tracks cumulative change from a reference point | Detecting slow shifts in process metrics |
| 15 | Holt-Winters (Triple Smoothing) | Adds seasonality and trend to exponential smoothing | Forecasting seasonally fluctuating data |
| 16 | Simple Lagged Features | Adds previous values as new columns | Enhancing model input with past behavior |
| 17 | Normalization/Standardization | Scales data to make it comparable or Gaussian | Preprocessing before regression or modeling |
| 18 | Clustering for Pattern Detection | Groups similar trends using K-means or DBSCAN | Discovering behavior groups in data |
| 19 | Savitzky–Golay Filter | Smooths data while preserving shape | Denoising noisy sensor or measurement data |
| 20 | Quantile Forecasting | Predicts a range (not just average) using percentiles | Risk modeling, demand estimation with bounds |
10 more
| # | Math Trick | Description | Use Case Example |
|---|---|---|---|
| 21 | Slope Calculation (Rate of Change) | Measures steepness between data points | Detecting acceleration in trends |
| 22 | Cross-Correlation | Measures similarity between two time series | Lag detection between related variables |
| 23 | Seasonality Index | Normalizes values by seasonal averages | Adjusting for repeating seasonal patterns |
| 24 | Principal Component Analysis (PCA) | Reduces dimensionality while preserving variance | Feature compression before modeling |
| 25 | Residual Analysis | Analyzes difference between actual and predicted values | Improving model accuracy by modeling errors |
| 26 | Bootstrapping | Resamples data with replacement to estimate confidence intervals | Estimating forecast uncertainty |
| 27 | Time Series Decomposition | Separates series into trend, seasonality, and residual | Understanding data components for forecasting |
| 28 | Interpolation | Fills in missing values between known data points | Reconstructing incomplete datasets |
| 29 | Weighted Least Squares (WLS) | Linear regression giving more weight to certain data points | Handling heteroscedasticity in data |
| 30 | Bayesian Updating | Updates forecast with new data based on prior beliefs | Dynamic forecasting as new data arrives |
| # | Math Trick | Description | Use Case Example |
|---|---|---|---|
| 31 | K-Nearest Neighbors (KNN) Forecasting | Predicts based on the average of similar past patterns | Forecasting similar behavior sequences |
| 32 | Dynamic Time Warping (DTW) | Measures similarity between time series with time shifts | Comparing sequences with misaligned timing |
| 33 | Prophet Model (by Facebook) | Decomposable time series model with trend, seasonality, holidays | Business forecasting with multiple components |
| 34 | Recurrent Patterns Detection | Identifies repeating patterns in time series | Analyzing periodic signals |
| 35 | Residual Smoothing | Smooths the error component of a forecast | Reducing noise in forecast residuals |
| 36 | Rolling Median | Like moving average, but uses median for robustness to outliers | Smoothing noisy data with outlier resistance |
| 37 | Data Binning | Groups continuous values into categories | Trend simplification or histogram generation |
| 38 | Signal Denoising (Wavelet Transform) | Removes high-frequency noise while preserving structure | Processing raw sensor or stock data |
| 39 | Granger Causality Test | Determines if one time series can predict another | Identifying causal predictors |
| 40 | Lead-Lag Analysis | Measures which variables lead or follow others in time | Input feature timing alignment |
| 41 | Change Point Detection | Detects shifts in trend or distribution | Finding when market behavior changes |
| 42 | Trend Strength Index | Quantifies how strong the trend is | Deciding if forecasting is appropriate |
| 43 | Signal-to-Noise Ratio (SNR) | Compares signal strength to background noise | Evaluating data quality for prediction |
| 44 | Elastic Net Regression | Combines Lasso and Ridge for robust regression | Forecasting with high-dimensional features |
| 45 | Lag Correlation Matrix | Compares correlation across time lags | Feature selection from past data |
| 46 | Time-Weighted Averages | Weighs recent data more heavily based on time decay | Real-time adaptive forecasting |
| 47 | Histogram-Based Forecasting | Predicts based on distribution of past values | Probabilistic forecasting from historical data |
| 48 | Rolling Standard Deviation | Tracks volatility over time | Measuring uncertainty or instability |
| 49 | Confidence Interval Forecasting | Predicts with upper and lower bounds | Risk-aware forecasting |
| 50 | Ensemble Averaging | Combines forecasts from multiple models | Improving accuracy through diversity |
