ARIMA
Alternatives
ETS
Exponential Smoothing
Model: y
t
= ℓ
t−1
+ b
t−1
+ s
t−m
+ ϵ
t
Use: Data with
trend and seasonality
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LSTM
Long Short-Term Memory networks
Model: h
t
= o
t
⊙ tanh(C
t
)
Use: Capturing long-
range dependencies
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VAR
Vector Autoregression
Model: y
t
= A
1
y
t−1
+ · · · + A
p
y
t−p
+ u
t
Use: Multiple inter-
related time series
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SVR
Support Vector Regression
Model: ˆy = w · ϕ(x) + b
Use: Time series regression tasks
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Prophet
Developed by Facebook
Model: y(t) = g(t) + s(t) + h(t) + ϵ
t
Use: Handling seasonality and
holidays (cf. Neural Prophet)
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XGBoost
Extreme Gradient Boosting
Model: Additive tree models
Use: Combined with feature
engineering for time series
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TBATS
Exponential smooth-
ing state space model
Model: y
t
= ℓ
t
+ s
t
+ ϵ
t
Use: Complex seasonal patterns
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