Time Series Forecasting
Model sequential temporal changes. Smooth historical noise using moving averages or exponential factors, and fit linear trend regressions to forecast future intervals.
Interactive Sandbox
Data Point Manipulation & Smoothed Projections
Drag any of the solid blue historical data points vertically to modify values. The smoothed fit line (emerald) and forecasted projection (dotted orange) will update in real-time.
1. Data Shape Preset
2. Smoothing Algorithm
Method:
Forecast Horizon:
SMA Period (k months):
3
3. Diagnostics Summary
Mean Absolute Error (MAE):
MAE = 4.31
Linear Trend Line:
y = 1.25x + 35.4
Algebraic Solver
Smoothing & Forecasting Trace Walkthrough
Trace how historical noise smoothing algorithms are calculated and how slope parameters are fitted to project future values.
Trace Calculations Steps
Historical vs. Smoothed Data Table
Evaluation
Time Series Quiz
Test your conceptual knowledge of trends, cyclical seasonality, smoothing variables, and phase lags.
Question 1 of 5
Score: 0/0
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Correct Answer!
Explanation text...
Time Series Cheat Sheet
- Trend: The long-term upward or downward direction of data (modeled as $y = mx + c$).
- Seasonality: Repeating cycles that occur within a calendar year or fixed interval (e.g. summer sales spikes).
- Smoothing: Moving averages filter out short-term random noise to reveal trends.
- Phase Lag: Smoothed lines always lag behind original peaks because they incorporate past historical data points.