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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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