ML predictions Phase 4: SARIMA spending forecast with dual confidence bands
Replaces unused Prophet dependency (unrunnable without cmdstan) with SARIMA (statsmodels SARIMAX) as the primary spending forecast algorithm. Strategy: SARIMA(1,1,1)(1,0,1,12) for 12+ months of data, ARIMA(1,1,1) for 6-11 months, Holt-Winters for 3-5 months, simple average below that. Adds 95% confidence bands (1.96σ) alongside existing 80% (1.28σ). Extends forecast horizon from 3 to 6 months and actuals display from 6 to 12 months. Each category now carries an algorithm field surfaced as a badge in the UI. Frontend chart shows both confidence tiers as stacked bar overlays with a 3-month summary grid below. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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4 changed files with 109 additions and 30 deletions
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@ -10,19 +10,55 @@ import pandas as pd
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warnings.filterwarnings("ignore")
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MIN_POINTS = 3
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FORECAST_MONTHS = 3
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FORECAST_MONTHS = 6
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def _next_month_starts(from_date: date, n: int) -> list[str]:
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months = []
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d = (from_date.replace(day=1) + relativedelta(months=1))
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d = from_date.replace(day=1) + relativedelta(months=1)
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for _ in range(n):
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months.append(d.strftime("%Y-%m-%d"))
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d += relativedelta(months=1)
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return months
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def _fit_holt(values: list[float], n: int) -> tuple[list[float], list[float], list[float]]:
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def _fit_sarima(values: list[float], n: int) -> tuple[list[float], list[float], list[float], list[float], list[float], str]:
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"""
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Primary algorithm. Uses SARIMAX with seasonal component when enough data exists,
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plain ARIMA otherwise. Returns (forecast, lower_80, upper_80, lower_95, upper_95, algorithm).
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"""
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from statsmodels.tsa.statespace.sarimax import SARIMAX
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series = np.array(values, dtype=float)
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algo = "sarima"
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try:
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if len(series) >= 12:
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# Seasonal ARIMA with annual period
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model = SARIMAX(series, order=(1, 1, 1), seasonal_order=(1, 0, 1, 12),
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enforce_stationarity=False, enforce_invertibility=False)
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else:
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model = SARIMAX(series, order=(1, 1, 1),
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enforce_stationarity=False, enforce_invertibility=False)
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fit = model.fit(disp=False, maxiter=200)
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forecast_obj = fit.get_forecast(steps=n)
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mean = forecast_obj.predicted_mean
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ci_80 = forecast_obj.conf_int(alpha=0.20) # 80% interval
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ci_95 = forecast_obj.conf_int(alpha=0.05) # 95% interval
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lower_80 = np.maximum(0, ci_80.iloc[:, 0].values).tolist()
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upper_80 = ci_80.iloc[:, 1].values.tolist()
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lower_95 = np.maximum(0, ci_95.iloc[:, 0].values).tolist()
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upper_95 = ci_95.iloc[:, 1].values.tolist()
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return mean.tolist(), lower_80, upper_80, lower_95, upper_95, algo
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except Exception:
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return _fit_holt(values, n)
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def _fit_holt(values: list[float], n: int) -> tuple[list[float], list[float], list[float], list[float], list[float], str]:
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"""Holt-Winters fallback."""
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from statsmodels.tsa.holtwinters import ExponentialSmoothing
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try:
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@ -36,13 +72,22 @@ def _fit_holt(values: list[float], n: int) -> tuple[list[float], list[float], li
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fit = model.fit(optimized=True, disp=False)
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forecast = fit.forecast(n)
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sigma = float(np.std(fit.resid)) if len(fit.resid) > 1 else float(np.mean(values) * 0.15)
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lower = np.maximum(0, forecast - 1.28 * sigma)
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upper = forecast + 1.28 * sigma
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return forecast.tolist(), lower.tolist(), upper.tolist()
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lower_80 = np.maximum(0, forecast - 1.28 * sigma).tolist()
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upper_80 = (forecast + 1.28 * sigma).tolist()
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lower_95 = np.maximum(0, forecast - 1.96 * sigma).tolist()
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upper_95 = (forecast + 1.96 * sigma).tolist()
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return forecast.tolist(), lower_80, upper_80, lower_95, upper_95, "holt_winters"
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except Exception:
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avg = float(np.mean(values))
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sigma = float(np.std(values)) if len(values) > 1 else avg * 0.15
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return [avg] * n, [max(0, avg - 1.28 * sigma)] * n, [(avg + 1.28 * sigma)] * n
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fcast = [avg] * n
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lower_80 = [max(0.0, avg - 1.28 * sigma)] * n
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upper_80 = [(avg + 1.28 * sigma)] * n
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lower_95 = [max(0.0, avg - 1.96 * sigma)] * n
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upper_95 = [(avg + 1.96 * sigma)] * n
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return fcast, lower_80, upper_80, lower_95, upper_95, "average"
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def forecast_spending(df: pd.DataFrame) -> list[dict]:
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@ -61,31 +106,47 @@ def forecast_spending(df: pd.DataFrame) -> list[dict]:
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group = group.sort_values("ds")
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values = group["y"].tolist()
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actuals = [
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{"date": row["ds"].strftime("%Y-%m-%d"), "amount": row["y"]}
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{"date": row["ds"].strftime("%Y-%m-%d"), "amount": round(float(row["y"]), 2)}
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for _, row in group.iterrows()
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]
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if len(values) < MIN_POINTS:
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avg = float(np.mean(values))
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sigma = avg * 0.15
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forecast_pts = [
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{"date": d, "amount": round(avg, 2), "lower": round(avg * 0.7, 2), "upper": round(avg * 1.3, 2)}
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{
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"date": d,
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"amount": round(avg, 2),
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"lower": round(max(0.0, avg - 1.28 * sigma), 2),
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"upper": round(avg + 1.28 * sigma, 2),
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"lower_95": round(max(0.0, avg - 1.96 * sigma), 2),
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"upper_95": round(avg + 1.96 * sigma, 2),
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}
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for d in future_dates
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]
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algo = "average"
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else:
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fcast, lower, upper = _fit_holt(values, FORECAST_MONTHS)
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fcast, lower_80, upper_80, lower_95, upper_95, algo = _fit_sarima(values, FORECAST_MONTHS)
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forecast_pts = [
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{"date": d, "amount": round(max(0, f), 2), "lower": round(l, 2), "upper": round(u, 2)}
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for d, f, l, u in zip(future_dates, fcast, lower, upper)
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{
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"date": d,
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"amount": round(max(0.0, f), 2),
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"lower": round(l80, 2),
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"upper": round(u80, 2),
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"lower_95": round(l95, 2),
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"upper_95": round(u95, 2),
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}
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for d, f, l80, u80, l95, u95 in zip(future_dates, fcast, lower_80, upper_80, lower_95, upper_95)
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]
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results.append({
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"category_id": cat_id,
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"category_id": str(cat_id),
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"category_name": cat_name,
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"monthly_avg": round(float(np.mean(values)), 2),
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"actuals": actuals[-6:], # last 6 months for display
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"algorithm": algo,
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"actuals": actuals[-12:], # last 12 months for display
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"forecast": forecast_pts,
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})
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# Sort by monthly_avg descending (highest spend first)
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results.sort(key=lambda x: x["monthly_avg"], reverse=True)
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return results
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@ -17,7 +17,6 @@ dependencies = [
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"qrcode[pil]>=8.0",
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"cryptography>=44.0",
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"yfinance>=0.2",
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"prophet>=1.1",
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"statsmodels>=0.14",
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"numpy>=2.0",
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"scipy>=1.14",
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@ -52,6 +51,7 @@ build-backend = "hatchling.build"
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[tool.pytest.ini_options]
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asyncio_mode = "auto"
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asyncio_default_fixture_loop_scope = "session"
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testpaths = ["tests"]
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[tool.hatch.build.targets.wheel]
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@ -4,8 +4,9 @@ export interface CategoryForecast {
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category_id: string;
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category_name: string;
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monthly_avg: number;
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algorithm: "sarima" | "holt_winters" | "average";
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actuals: { date: string; amount: number }[];
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forecast: { date: string; amount: number; lower: number; upper: number }[];
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forecast: { date: string; amount: number; lower: number; upper: number; lower_95: number; upper_95: number }[];
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}
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export interface SpendingForecastResponse {
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@ -205,6 +205,12 @@ function BudgetForecastCard({ forecast: f }: { forecast: import("@/api/predictio
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// ─── Spending Forecast ───────────────────────────────────────────────────────
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const ALGO_LABELS: Record<string, string> = {
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sarima: "SARIMA",
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holt_winters: "Holt-Winters",
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average: "Avg",
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};
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function SpendingTab() {
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const { data, isLoading } = useQuery({ queryKey: ["pred-spending"], queryFn: getSpendingForecast });
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const [selected, setSelected] = useState(0);
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@ -218,8 +224,10 @@ function SpendingTab() {
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...cat.forecast.map(p => ({
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date: p.date.slice(0, 7),
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forecast: p.amount,
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lower: p.lower,
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upper: p.upper,
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lower_80: p.lower,
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upper_80: p.upper,
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lower_95: p.lower_95,
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upper_95: p.upper_95,
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})),
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];
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@ -246,29 +254,38 @@ function SpendingTab() {
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<div className="bg-card border border-border rounded-xl p-5">
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<div className="flex items-center justify-between mb-4">
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<p className="text-sm font-semibold">{cat.category_name} — Spending Forecast</p>
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<p className="text-xs text-muted-foreground">Shaded = 80% confidence interval</p>
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<div className="flex items-center gap-2">
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<p className="text-sm font-semibold">{cat.category_name} — 6-Month Forecast</p>
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<span className="text-xs bg-secondary text-muted-foreground px-2 py-0.5 rounded-full">
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{ALGO_LABELS[cat.algorithm] ?? cat.algorithm}
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</span>
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</div>
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<p className="text-xs text-muted-foreground">Dark = 80% · Light = 95% confidence</p>
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</div>
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<ResponsiveContainer width="100%" height={260}>
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<ResponsiveContainer width="100%" height={280}>
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<BarChart data={chartData} margin={{ top: 5, right: 10, left: 0, bottom: 5 }}>
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<XAxis dataKey="date" tick={{ fontSize: 10, fill: "hsl(var(--muted-foreground))" }} stroke="hsl(var(--muted-foreground))" />
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<YAxis tick={{ fontSize: 10, fill: "hsl(var(--muted-foreground))" }} stroke="hsl(var(--muted-foreground))" tickFormatter={v => `£${v}`} width={55} />
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<Tooltip {...TOOLTIP_STYLE} formatter={(v: number) => formatCurrency(v, "GBP")} />
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<Tooltip {...TOOLTIP_STYLE} formatter={(v: number, name: string) => [formatCurrency(v, "GBP"), name]} />
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<Bar dataKey="actual" fill="hsl(var(--primary))" name="Actual" radius={[2, 2, 0, 0]} />
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<Bar dataKey="forecast" fill="hsl(var(--primary) / 0.5)" name="Forecast" radius={[2, 2, 0, 0]} />
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<Bar dataKey="forecast" fill="hsl(var(--primary) / 0.55)" name="Forecast" radius={[2, 2, 0, 0]} />
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<Bar dataKey="upper_95" fill="hsl(var(--primary) / 0.10)" name="95% upper" radius={[2, 2, 0, 0]} legendType="none" />
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<Bar dataKey="upper_80" fill="hsl(var(--primary) / 0.20)" name="80% upper" radius={[2, 2, 0, 0]} legendType="none" />
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</BarChart>
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</ResponsiveContainer>
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{/* Confidence band as area overlay */}
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{cat.forecast.length > 0 && (
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<div className="mt-2 text-xs text-muted-foreground text-center">
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Forecast next 3 months: {cat.forecast.map(f =>
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`${f.date.slice(0, 7)}: ${formatCurrency(f.amount, "GBP")} (${formatCurrency(f.lower, "GBP")}–${formatCurrency(f.upper, "GBP")})`
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).join(" · ")}
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<div className="mt-3 grid grid-cols-3 gap-2">
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{cat.forecast.slice(0, 3).map(f => (
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<div key={f.date} className="bg-secondary/40 rounded-lg px-3 py-2 text-center">
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<p className="text-xs text-muted-foreground mb-0.5">{f.date.slice(0, 7)}</p>
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<p className="text-sm font-semibold tabular-nums">{formatCurrency(f.amount, "GBP")}</p>
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<p className="text-xs text-muted-foreground">{formatCurrency(f.lower_95, "GBP")}–{formatCurrency(f.upper_95, "GBP")}</p>
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</div>
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))}
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</div>
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)}
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</div>
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</div>
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);
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}
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