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Running arima time series forecasting analysis...
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Analyze another fileFits ARIMA models to time series data and generates forecasts with confidence intervals. Automatically selects optimal model parameters (p, d, q) or lets you specify them manually.
Use this when you have a single time series (daily, weekly, or monthly) and want a statistical forecast.
If you have multiple seasonal patterns, use Prophet Decomposition. If you just need a visual trend, use Simple Trend.
Built for: Forecasting analyst, demand planner, financial analyst, operations manager
Typical data source: Time-stamped numeric data — daily sales, weekly revenue, monthly users, etc.
Time series data
Minimum 50 rows · Best with 100-1000 time points
Fit ARIMA models to time series data and generate forecasts with confidence intervals. Automatically selects optimal model order (p, d, q) via AIC minimization. Includes decomposition, ACF/PACF diagnostics, residual analysis, and tabular forecast output.
Historical data with future forecast and confidence bands
Trend, seasonal, and residual components
Autocorrelation and partial autocorrelation functions
Model residuals over time (white noise indicates good fit)
MAPE, RMSE, MAE, and other accuracy metrics
ARIMA model order and estimated coefficients
Tabular forecast values with confidence intervals
Plain-English interpretation — what the numbers mean, what's significant, and what to do next.
Need something simpler? Simple Trend — Just need a trend visualization
Need more power? Prophet Decomposition — Multiple seasonalities or changepoints
Similar: Time Series Forecast
See our FAQ for details on pricing, data privacy, and how the analysis works. Every report includes a Methodology section showing the statistical test, assumptions checked, and diagnostics run.
Run any analysis on your own data — 60+ validated R modules, interactive reports, AI insights, and PDF export.
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