---
name: ARIMA Time Series Model
slug: arima-time-series-model
category: Data
description: ARIMA Time Series Model builds ARIMA or SARIMA forecasts from univariate historical data. Use it to test stationarity, choose parameters, and produce predictions with confidence intervals.
github: "https://github.com/asgard-ai-platform/skills/tree/main/algo-forecast-arima"
language: Python
stars: 228
forks: 28
install: "npx degit https://github.com/asgard-ai-platform/skills/tree/main/algo-forecast-arima ~/.claude/skills/algo-forecast-arima"
installs_to: ~/.claude/skills/algo-forecast-arima
source_path: algo-forecast-arima/SKILL.md
collection_size: 25
category_size: 710
collection_url: "https://dirskills.com/collections/asgard-ai-platform/skills"
added: 2026-09-03T06:04:49.656Z
last_synced: 2026-09-03T06:04:49.656Z
canonical_url: "https://dirskills.com/skills/arima-time-series-model"
---

# ARIMA Time Series Model

ARIMA Time Series Model builds ARIMA or SARIMA forecasts from univariate historical data. Use it to test stationarity, choose parameters, and produce predictions with confidence intervals.

**Install:**

```bash
npx degit https://github.com/asgard-ai-platform/skills/tree/main/algo-forecast-arima ~/.claude/skills/algo-forecast-arima
```

## README

# ARIMA Time Series Model

## Overview

ARIMA(p,d,q) combines autoregression (AR), differencing (I), and moving average (MA) for time series forecasting. Seasonal variant: SARIMA(p,d,q)(P,D,Q,s). Requires stationary data (achieved through differencing). Best for univariate series with clear trend/seasonality patterns.

## When to Use

**Trigger conditions:**
- Forecasting univariate time series (sales, demand, traffic)
- Data has clear trend and/or seasonal patterns
- Need interpretable model with statistical properties

**When NOT to use:**
- For multivariate forecasting with many external features (use ML models)
- For very long-range forecasts (ARIMA confidence intervals widen rapidly)
- For irregular/event-driven data (use causal models)

## Algorithm

```
IRON LAW: ARIMA Requires STATIONARY Data
Non-stationary data (trend, changing variance) violates ARIMA assumptions.
Test stationarity with ADF test (p < 0.05 = stationary).
If non-stationary: difference the series (d=1 usually suffices).
If still non-stationary after d=2, ARIMA may not be appropriate.
```

### Phase 1: Input Validation
Check: regular time intervals, no missing values (impute if needed), minimum 50 observations (ideally 2+ full seasonal cycles). Test stationarity with ADF test.
**Gate:** Data is regular, sufficient length, stationarity assessed.

### Phase 2: Core Algorithm
1. **Stationarity**: ADF test. If p > 0.05, difference (d=1). Retest.
2. **Parameter selection**: Examine ACF/PACF plots. Or use auto_arima (AIC-based grid search).
   - p (AR terms): PACF cutoff lag
   - q (MA terms): ACF cutoff lag
   - d: number of differences needed
3. **Fit model**: Maximum likelihood estimation
4. **Forecast**: Generate predictions with confidence intervals

### Phase 3: Verification
Check residuals: should be white noise (no autocorrelation). Ljung-Box test (p > 0.05 = no autocorrelation). Residuals normally distributed.
**Gate:** Residuals pass Ljung-Box test, no remaining patterns.

### Phase 4: Output
Return forecasts with confidence intervals.

## Output Format

```json
{
  "forecasts": [{"period": "2025-04", "forecast": 1250, "lower_95": 1100, "upper_95": 1400}],
  "model": {"order": [1,1,1], "seasonal_order": [1,1,1,12], "aic": 520.3},
  "metadata": {"training_periods": 60, "forecast_horizon": 12}
}
```

## Examples

### Sample I/O
**Input:** 12 monthly observations with upward trend: [10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32]

**Step 1:** First difference = [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2] (constant → stationary, d=1 sufficient)

**Step 2:** ARIMA(0,1,0) random walk with drift μ=2 is the simplest fitting model.

**Expected forecast (ARIMA(0,1,0) with drift=2):**
- Period 13: 32 + 2 = **34**
- Period 14: 32 + 4 = **36**
- Period 15: 32 + 6 = **38**

Verify: differenced series is constant (2) → no AR/MA terms needed. Residuals are exactly 0 → perfect fit (toy example). On real data, residuals should pass Ljung-Box (p > 0.05).

### Edge Cases
| Input | Expected | Why |
|-------|----------|-----|
| No trend, no seasonality | ARIMA(p,0,q) | No differencing needed |
| Strong trend only | ARIMA(p,1,q) | Single difference removes linear trend |
| Multiple seasonalities | ARIMA may struggle | Consider Prophet or TBATS instead |

## Gotchas

- **Over-differencing**: d=2 when d=1 suffices introduces unnecessary noise. Check if first difference is stationary before differencing again.
- **Auto-ARIMA isn't magic**: AIC-based selection can pick overfit models. Always check residual diagnostics regardless of auto selection.
- **Confidence intervals widen fast**: Multi-step forecasts accumulate uncertainty. Don't trust point forecasts beyond 2-3 seasonal cycles.
- **Calendar effects**: Business days, holidays, and leap years affect monthly/weekly data. ARIMA doesn't handle these natively — add regressors or use Prophet.
- **Structural breaks**: ARIMA assumes the data-generating process is stable. COVID, market shocks, or policy changes break this assumption.

## References

- For ACF/PACF interpretation guide, see `references/acf-pacf.md`
- For SARIMA seasonal parameter selection, see `references/seasonal-arima.md`