Example of daily predictions with weekly seasonality

Suppose we have daily observations that we wish to model with weekly seasonality. The following is a simple example of how to build a time series, a model, and make predictions using such data.

import com.github.signaflo.math.stats.distributions.Normal;
import com.github.signaflo.timeseries.TimePeriod;
import com.github.signaflo.timeseries.TimeSeries;
import com.github.signaflo.timeseries.model.arima.Arima;
import com.github.signaflo.timeseries.model.arima.ArimaOrder;
import com.github.signaflo.timeseries.forecast.Forecast;

// First, we'll fill in 15 weeks worth of daily data with an extremely simple
// simulated data generating process.
Normal normal = new Normal(); // Create a normal distribution with mean 0 an sd of 1.

double[] values = new double[105];
for (int i = 0; i < values.length; i++) {
  values[i] = normal.rand();
}

// Assumes Monday corresponds to 0.
for (int fri = 4; fri < values.length; fri += 7) {
  values[fri] += 1.0;
  values[fri + 1] += 2.0;
  values[fri + 2] -= 1.0;
}

// Second, we'll create a daily time series from those values.
TimePeriod day = TimePeriod.oneDay();

TimeSeries series = TimeSeries.from(day, values);

// Third, we'll create an ArimaOrder with a seasonal component.
ArimaOrder order = ArimaOrder.order(0, 0, 0, 1, 1, 1);

// Fourth, we create an ARIMA model with the series, the order,
// and the weekly seasonality.

TimePeriod week = TimePeriod.oneWeek();

Arima model = Arima.model(series, order, week);

// Finally, generate a forecast for next week using the model

Forecast forecast = model.forecast(7);