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<h1 id="basicforecaster">BasicForecaster</h1>
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<p><em>Basic forecasting functions for multivariate time series (mean, median, random walk)</em></p>
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<p><span style="float:right;"><a href="https://github.com/Techtonique/ahead/ahead/Basic/BasicForecaster.py#L45">[source]</a></span></p>
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<h3 id="basicforecaster_1">BasicForecaster</h3>
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<pre><code class="language-python">ahead.Basic.BasicForecaster.BasicForecaster(
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h=5, level=95, method=&quot;mean&quot;, type_pi=&quot;gaussian&quot;, B=100, date_formatting=&quot;original&quot;, seed=123
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)
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</code></pre>
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<p>Basic forecasting functions for multivariate time series (mean, median, random walk)</p>
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<p>Parameters:</p>
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<pre><code>h: an integer;
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forecasting horizon
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level: an integer;
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Confidence level for prediction intervals
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method: a string;
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Forecasting method, either "mean", "median", or random walk ("rw")
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type_pi: a string;
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Type of prediction interval (currently "gaussian",
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or "bootstrap")
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B: an integer;
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Number of bootstrap replications for `type_pi == bootstrap`
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date_formatting: a string;
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Currently:
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- "original": yyyy-mm-dd
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- "ms": milliseconds
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seed: an integer;
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reproducibility seed for type_pi == 'bootstrap'
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</code></pre>
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<p>Attributes:</p>
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<pre><code>fcast_: an object;
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raw result from fitting R's `ahead::ridge2f` through `rpy2`
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averages_: a list of lists;
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mean forecast in a list for each series
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ranges_: a list of lists;
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lower and upper prediction intervals in a list for each series
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output_dates_: a list;
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a list of output dates (associated to forecast)
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mean_: a numpy array
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contains series mean forecast as a numpy array
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lower_: a numpy array
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contains series lower bound forecast as a numpy array
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upper_: a numpy array
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contains series upper bound forecast as a numpy array
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result_dfs_: a tuple of data frames;
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each element of the tuple contains 3 columns,
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mean forecast, lower + upper prediction intervals,
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and a date index
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sims_: currently a tuple of numpy arrays
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for `type_pi == bootstrap`, simulations for each series
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</code></pre>
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<p>Examples:</p>
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<pre><code class="language-python">import pandas as pd
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from ahead import BasicForecaster
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# Data frame containing the time series
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dataset = {
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'date' : ['2001-01-01', '2002-01-01', '2003-01-01', '2004-01-01', '2005-01-01'],
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'series1' : [34, 30, 35.6, 33.3, 38.1],
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'series2' : [4, 5.5, 5.6, 6.3, 5.1],
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'series3' : [100, 100.5, 100.6, 100.2, 100.1]}
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df = pd.DataFrame(dataset).set_index('date')
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print(df)
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# multivariate time series forecasting
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r1 = BasicForecaster(h = 5)
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r1.forecast(df)
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print(r1.result_dfs_)
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</code></pre>
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<hr />
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<p><span style="float:right;"><a href="https://github.com/Techtonique/ahead/ahead/Basic/BasicForecaster.py#L156">[source]</a></span></p>
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<h3 id="forecast">forecast</h3>
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<pre><code class="language-python">BasicForecaster.forecast(df)
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</code></pre>
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<p>Forecasting method from <code>BasicForecaster</code> class</p>
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<p>Parameters:</p>
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<pre><code>df: a data frame;
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a data frame containing the input time series (see example)
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</code></pre>
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