CODEWITHSUNDEEP
pythonpandasarimapmdarima
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Reputation: 3

Does auto arima predict function return series

I have been following this tutorial: https://www.analyticsvidhya.com/blog/2021/11/basic-understanding-of-time-series-modelling-with-auto-arimax/

I was fitting it on my dataset. It was working fine but when I ran it again the next day model.predict function started returning series rather than a list. And it is not giving proper results like it did previously.

model = pm.auto_arima(df_train["farm rate"], exogenous=df_train[exogenous_features], trace=True, error_action="ignore", suppress_warnings=True)
model.fit(df_train["farm rate"], exogenous=df_train[exogenous_features])

forecast = model.predict(n_periods=len(df_valid), exogenous=df_valid[exogenous_features])
df_valid["Forecast_ARIMAX"] = forecast

My df looks like this

                date    day doc farm rate   open    close   price   month   year    day_of_week week    doc_mean_3  open_mean_3 close_mean_3    price_mean_3
date                                                            
2015-01-01  2015-01-01  1.0 51.5    165.0   170.0   170.0   2375.0  1.0 2015.0  3.0 1.0 38.574580   155.158973  150.355472  3418.872350
2015-01-02  2015-01-02  2.0 48.5    165.0   170.0   160.0   2375.0  1.0 2015.0  4.0 1.0 38.574580   155.158973  150.355472  3418.872350
2015-01-03  2015-01-03  3.0 44.5    150.0   180.0   167.5   2375.0  1.0 2015.0  5.0 1.0 48.166667   173.333333  165.833333  2375.000000
2015-01-05  2015-01-05  5.0 40.5    150.0   165.0   155.0   2375.0  1.0 2015.0  0.0 2.0 44.500000   171.666667  160.833333  2375.000000
2015-01-06  2015-01-06  6.0 36.5    140.0   155.0   152.5   2375.0  1.0 2015.0  1.0 2.0 40.500000   166.666667  158.333333  2375.000000
2015-01-07  2015-01-07  7.0 35.5    140.0   155.0   150.0   2375.0  1.0 2015.0  2.0 2.0 37.500000   158.333333  152.500000  2375.000000
2015-01-08  2015-01-08  8.0 35.5    140.0   162.5   155.0   2375.0  1.0 2015.0  3.0 2.0 35.833333   157.500000  152.500000  2375.000000
2015-01-09  2015-01-09  9.0 37.5    145.0   165.0   157.5   2375.0  1.0 2015.0  4.0 2.0 36.166667   160.833333

I have data from 2015 to 2022.Exogenous features are : ["doc", "open","close","doc_mean_3", "open_mean_3", "close_mean_3", "price_mean_3"] They are calculated using this method:

def calc_lag_features(df, feature, window):
  
    df[f"{feature}_mean_{window}"] = df[feature].rolling(window=window).mean()
    return df

Edit: These are the results I get. Completely flat graph. Adding or removing exogenous variables isnt making any difference. Previously, it was able to capture the true graph to quite an extent. OUTPUT

Depending on exogenous variables straight line only goes up and down and is not able to capture the true trend.

Upvotes: 0

Views: 170

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