x + by + cz + d = 0 a (f(x + T) = f(x), ∀ x ∈ IR) (f(−x) = f(x), ∀ x ∈ D) −x + 2y + z + t = −1 2x + 2y + z + 4t = 0 C = {z = x + iy x, y ∈ IR} z = −1 + i nα = θ + 2πk

Network Time-Series Forecasting

Managing complex systems composed of multiple interconnected elements that are influenced by external factors is a difficult task. Today technology allows to monitor what is happening on every single element of a complex network, but for optimal planning an additional step is necessary: making forecasts on different time horizons and with different degrees of detail.

  • Description and benefits
  • Application examples
Maintenance planning
Reacting effectively to unexpected phenomena
Increase speed of action

Application examples of Network Time-Series Forecasting

IT
Energy & Utilities
Media & ADV

Operating logic

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z = −1 + i nα = θ + 2πk u = ρ (cosα + isinα) f(x) = 3x − 2 z = r (cosθ + isinθ) ax + by + cz + d = 0 a (f(x + T) = f(x), ∀ x ∈ IR) (f(−x) = f(x), ∀ x ∈ D) 2x + 2y + z + 4t = 0 [x0 −r, x0 + r] (cosnα + isinnα) = (cosα + isinα)n z = −1 + i nα = θ + 2πk u = ρ (cosα + isinα)

The solution for Terna