KDDM2 Final PresentationTime Series Modelling

Karin MaierJune 24, 2017

Individual Household power consumption

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Approach

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2. Stationarity

Check• Visual Inspection• Dickey Fuller Test

Stationarize if necessary• log• differencing

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2. Stationarity

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3. Parameter Selection

With Python statsmodels ARIMA time series fitting seems easy,only need to set ARIMA(p,d,q)...

Find p,q how?

• AC/PACF• Gridsearch

...Minimum AIC found with: [-7.607632985789451, (6, 0, 4)]

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4. Fit your model

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4. Fit your model

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5. Predict and Forecast

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5. Predict and Forecast

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Loosing information?

The longer the forecasting period, the higher the error.

Models with resampled weekly and monthly data perform betterthan daily data model in 1 week+ forecasts.

Really think about what you want to model/forecast.

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Discussion/New Insights

• How good is good enough?• Where to start over when improving? At stationarity, the

model, the parameters of your model?• Or even with the data you fit/resampling?

Tinkering on your model takes time and/or knowledge.

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Don’t be afraid to try time series modelling.But maybe don’t start with all your savings at

the stock market...Thank you!

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