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In-Browser Linear Regression, Exponential Smoothing & Time-Series Forecaster (2026)

Forecast time-series metrics locally in your browser: run Ordinary Least Squares (OLS) Linear/Polynomial Regression, Holt's Double Exponential Smoothing, Simple Moving Averages, 95% Prediction Intervals, and RMSE/MAE/MAPE accuracy backtesting.Documentation & FAQs ↓

In-Browser Linear Regression, Exponential Smoothing & Time-Series Forecaster — Interactive Console
Runs locally in your browser • Instant output
Forecast Horizon (Future Periods)+6 periods
Exponential Smoothing Alpha (α)0.45
OLS Linear Trend
y = 1629.02t + 11219.7
R² Goodness of Fit
0.9654
RMSE Error
±1,064.9
MAPE Accuracy
4.44% error
Historical Trajectory + 6-Period Holt/OLS Forecast & 95% CISolid = Historical | Dashed = Forecast
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Blog / Documentation HTML Citation<a href="https://www.zerosuniverse.com/tools/time-series-regression-forecasting-studio/">In-Browser Linear Regression, Exponential Smoothing & Time-Series Forecaster — ZerosUniverse</a>

2026 Quick-Reference Cheat Sheet & Benchmark Table: In-Browser Linear Regression, Exponential Smoothing & Time-Series Forecaster

Quick Answer & 2026 Technical Summary (time series forecasting linear regression calculator)Updated 2026 Standard

Ordinary Least Squares (OLS) Linear Regression assigns equal weight to every historical data point from `t=1` to `t=N` to fit a single straight line (`y = b0 + b1*t`). Holt's Double Exponential Smoothing uses two recursive smoothing parameters—`alpha` (level) and `beta` (trend slope)—that decay exponentially into the past, allowing the forecast to adapt rapidly when growth accelerates or decelerates recently. Use this interactive time series forecasting linear regression calculator above to test holt exponential smoothing forecast calculator, ols linear regression r squared confidence interval, and rmse mae mape forecast accuracy calculator locally in your browser with zero server uploads.

Target Keyword Spec: time series forecasting linear regression calculator | Modules: Multi-Model Forecasting Engine (OLS Linear, Holt Smoothing, Log-Linear & SMA) • 95% Prediction Interval Fan Chart & SVG Trend Visualizer • Comprehensive Error & Goodness-of-Fit Metrics (R², RMSE, MAE, MAPE)
Primary Focus: time series forecasting linear regression calculator
Core Capability: holt exponential smoothing forecast calculator
Privacy Mode: 100% Client-Side (Zero Upload)
Technical Parameter / ModuleStandard / Keyword SpecArchitecture & Validation RuleOperational Use Case (2026)
Multi-Model Forecasting Engine (OLS Linear, Holt Smoothing, Log-Linear & SMA)holt exponential smoothing forecast calculatorCompare Ordinary Least Squares (y = mx + b), Holt's Two-Parameter Double Ex...SaaS ARR, MRR & Traffic Growth Forecasting
95% Prediction Interval Fan Chart & SVG Trend Visualizerols linear regression r squared confidence intervalRender historical observations, fitted model curves, future horizon project...Comparing Linear Trend vs Adaptive Exponential Smoothing
Comprehensive Error & Goodness-of-Fit Metrics (R², RMSE, MAE, MAPE)rmse mae mape forecast accuracy calculatorEvaluate model fidelity with Coefficient of Determination (R²), Adjusted R²...Capacity Planning & Inventory Demand Backtesting
Tokenizer & Model Architecturetiktoken (o200k_base / cl100k_base) + GGUF1 Token ≈ 0.75 English Words (~4 Chars)Calibrated for 2026 Frontier & Open-Weight LLMs
Context Window & KV Cache Scaling8k / 32k / 128k / 1M+ Token ContextsFP16 vs Q8_0 vs Q4_K_M QuantizationAccounts for FlashAttention & prompt caching
Inference Cost & Throughput MetricUSD per 1M Input / Cached / Output TokensMemory Bandwidth (GB/s) ÷ Model Size (GB)Optimizes self-hosted GPU vs cloud API ROI
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Step-by-Step Workflow

4 Easy Steps
01Phase 1

Enter Time-Series Values or Select a Real-World Benchmark Preset

Paste comma- or newline-separated numeric values (or `Period, Value` pairs), or load a preset (SaaS MRR Growth, Cloud GPU Demand, E-Commerce Seasonal Sales).

02Phase 2

Select Forecasting Algorithm & Smoothing Hyperparameters

Choose OLS Linear Regression, Holt Double Exponential Smoothing (tune Level alpha `0.1–0.9` and Trend beta `0.05–0.5`), Log-Exponential Trend, or Moving Average, and set your Forecast Horizon `h`.

03Phase 3

Inspect the Interactive SVG Forecast & 95% Confidence Fan

Examine how the fitted line tracks historical points and observe the expected forecast trajectory alongside the 95% upper and lower prediction bounds.

04Phase 4

Compare R², MAPE & RMSE in the Model Leaderboard Table

Review the automatic side-by-side accuracy comparison across all models and export the forecast table to CSV.

Real-World Applications

SaaS ARR, MRR & Traffic Growth Forecasting

Project 3 to 12 months of future recurring revenue, organic search sessions, or cloud server utilization with statistically grounded upper and lower confidence bounds.

Comparing Linear Trend vs Adaptive Exponential Smoothing

Test whether your dataset follows a rigid global linear slope (OLS) or benefits from Holt's alpha/beta smoothing that weights recent momentum more heavily.

Capacity Planning & Inventory Demand Backtesting

Compare MAPE and RMSE across forecasting methods to select the lowest-error estimator for supply chain or Kubernetes cluster scaling.

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