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47 lines
1.6 KiB
47 lines
1.6 KiB
{
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"metadata": {
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": 3
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},
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"orig_nbformat": 2
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},
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"nbformat": 4,
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"nbformat_minor": 2,
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"cells": [
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{
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"source": [
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"# Time series forecasting with ARIMA\n",
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"\n",
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"In this notebook, we demonstrate how to:\n",
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"- prepare time series data for training an ARIMA time series forecasting model\n",
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"- implement a simple ARIMA model to forecast the next HORIZON steps ahead (time *t+1* through *t+HORIZON*) in the time series\n",
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"- evaluate the model \n",
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"\n",
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"\n",
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"The data in this example is taken from the GEFCom2014 forecasting competition<sup>1</sup>. It consists of 3 years of hourly electricity load and temperature values between 2012 and 2014. The task is to forecast future values of electricity load. In this example, we show how to forecast one time step ahead, using historical load data only.\n",
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"\n",
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"<sup>1</sup>Tao Hong, Pierre Pinson, Shu Fan, Hamidreza Zareipour, Alberto Troccoli and Rob J. Hyndman, \"Probabilistic energy forecasting: Global Energy Forecasting Competition 2014 and beyond\", International Journal of Forecasting, vol.32, no.3, pp 896-913, July-September, 2016."
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],
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"cell_type": "markdown",
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"metadata": {}
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"pip install statsmodels"
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]
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}
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]
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} |