10 fun math tricks for predicting the future with forecasts

#	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