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Here is the type of data I have:
date a_kg b_kg a_gbp b_gbp t_kg year
2020-01-05T00:00:00.000+00:00 4123620.356542087 5430179.407605753 2777354.579378964 2298985.614845754 11652081.086148437 2020
2020-01-12T00:00:00.000+00:00 5852142.919229749 3682096.494749166 2542698.063547792 3783111.968057487 12272639.099464454 2020
2020-01-19T00:00:00.000+00:00 5195981.825434215 3260206.371941118 3657475.0183038586 3901962.6215422433 8226024.324803047 2020
2020-01-26T00:00:00.000+00:00 4795975.45259111 6795542.149013333 2713506.6533871787 3403192.9939237405 6770363.471693737 2020
2020-02-02T00:00:00.000+00:00 3468055.9213273097 6862528.132298238 2561869.0193747617 3927417.9498864026 7595546.137793591 2020
2020-02-09T00:00:00.000+00:00 3467983.561008608 6233589.392465845 3085392.166316497 3234488.990910977 8989754.520383794 2020
2020-02-16T00:00:00.000+00:00 3174250.836504598 4218455.076693483 2281848.4499495253 3306832.0734549854 11726103.361457452 2020
2020-02-23T00:00:00.000+00:00 5598528.437324805 3390688.4560255352 3604393.9615080794 3068852.545896374 12025114.082794404 2020
2020-03-01T00:00:00.000+00:00 4803345.035229626 5736932.106048628 2149101.2873595417 2063547.816860238 6048664.913718335 2020
2020-03-08T00:00:00.000+00:00 5124217.733388137 4760609.974958405 3973773.8732010345 2269728.567483261 9575231.11804296 2020
2020-03-15T00:00:00.000+00:00 3061753.4828874073 3488152.939379115 3544489.5385933146 2078572.9642168356 8921877.022041453 2020
2020-03-22T00:00:00.000+00:00 5909729.556485983 4980707.640445081 2397431.363068345 3591026.0281594507 7554754.673295111 2020
2020-03-29T00:00:00.000+00:00 5497327.922401265 3137554.0844608736 2011044.2342472048 2785889.9526908165 6839057.5713357795 2020
2020-04-05T00:00:00.000+00:00 3637017.3320348286 6637281.608315129 3630922.856909668 3271426.727911757 8363306.199825396 2020
2020-04-12T00:00:00.000+00:00 3545474.9016213017 4035119.9264000678 3413714.687695234 4268916.184815233 12600367.927387634 2020
2020-04-19T00:00:00.000+00:00 3550213.5295603015 5650089.137415928 3458014.3360819747 2623230.5728721875 8262420.5241452865 2020
2020-04-26T00:00:00.000+00:00 3912726.728878613 4246844.304357644 3542540.6933718915 3025957.3075890746 9631534.352203563 2020
2020-05-03T00:00:00.000+00:00 4574269.294896713 5080272.084711243 2148089.303468181 3888877.8463576217 10921132.712266244 2020
2020-05-10T00:00:00.000+00:00 4295835.055926347 5186841.117373118 2716931.457088545 2571995.4137290563 8545407.216655057 2020
2020-05-17T00:00:00.000+00:00 3873687.420594126 3739417.822102108 2231738.1190502592 2192449.7745719827 12802474.579046724 2020
2020-05-24T00:00:00.000+00:00 4835558.684167138 6878338.511058234 3726206.851751187 2724378.63228442 12737131.06459478 2020
2020-05-31T00:00:00.000+00:00 3418481.5819561253 6100531.293444458 3246596.253655116 2403053.218135011 7762476.070777549 2020
2020-06-07T00:00:00.000+00:00 3876433.9456056543 6757995.766256757 2661796.0497052986 4324244.130856432 9480739.541246697 2020
2020-06-14T00:00:00.000+00:00 4099085.529881075 6579309.401710596 2127116.7005720474 4020300.9489110424 8106148.168717388 2020
2020-06-21T00:00:00.000+00:00 4368209.952651108 5391599.915244341 2621964.6434313245 3583509.3912760587 7993883.460642274 2020
2020-06-28T00:00:00.000+00:00 5355527.884179041 6687496.940092467 2650366.644053494 4178651.4754692945 6258208.631481729 2020
2020-07-05T00:00:00.000+00:00 3599021.346475079 3353970.008207678 3459212.356676128 4009180.192247786 10266950.337859277 2020
2020-07-12T00:00:00.000+00:00 4542703.315240835 3783931.4496765807 3275114.942710426 2466425.1472150898 9518753.16260203 2020
2020-07-19T00:00:00.000+00:00 4777243.706586127 3180909.1556421523 3774425.4851526534 4231397.4962249445 6360351.2587499255 2020
2020-07-26T00:00:00.000+00:00 3139351.238159993 4301321.323053057 2944429.8503238987 3348355.6047891267 7950525.2496562805 2020
Here is the code I have:
# Ensure the 'date' column exists in the DataFrame
if 'date' in weekly_dt_filt.columns:
# Convert 'date' column to datetime
weekly_dt_filt['date'] = pd.to_datetime(weekly_dt_filt['date'])
# Set 'date' column as the index
weekly_dt_filt.set_index('date', inplace=True)
else:
raise KeyError("The 'date' column is not found in the DataFrame")
# Aggregate weekly data to monthly
df_monthly = weekly_dt_filt.groupby(['year', 'month']).agg({'a_kg': 'sum'}).reset_index()
# Convert DataFrame to Darts TimeSeries
series_weekly = TimeSeries.from_dataframe(weekly_dt_filt, value_cols='a_kg')
series_monthly = TimeSeries.from_dataframe(df_monthly, value_cols='a_kg')
# Split into train-test sets
train_weekly, test_weekly = series_weekly.split_before(0.8)
train_monthly, test_monthly = series_monthly.split_before(0.8)
This is another code
# Define models
models = {
'ExponentialSmoothing': [
ExponentialSmoothing(trend='add', seasonal='add', seasonal_periods=52),
ExponentialSmoothing(trend='add', seasonal='mul', seasonal_periods=12)
],
'SeasonalARIMA': [
ARIMA(p=1, d=1, q=1, seasonal_order=(1, 1, 1, 52)),
ARIMA(p=1, d=1, q=1, seasonal_order=(1, 1, 1, 12))
],
'FFT': [
FFT(nr_freqs_to_keep=10),
FFT(nr_freqs_to_keep=5)
]
}
def evaluate_models(train, test, model_list):
performance = []
for model in model_list:
start_time = time.time()
model.fit(train)
forecast = model.predict(len(test))
end_time = time.time()
# Ensure forecast and test are TimeSeries objects
if not isinstance(forecast, TimeSeries):
raise ValueError(f"Forecast is not a TimeSeries object: {forecast}")
if not isinstance(test, TimeSeries):
raise ValueError(f"Test is not a TimeSeries object: {test}")
performance.append({
'Model': type(model).__name__,
'MAE': mae(test, forecast),
'MSE': mse(test, forecast),
'MASE': mase(test, forecast, train),
'Forecast Bias': (forecast.mean() - test.mean()).values()[0],
'Time Elapsed (s)': end_time - start_time
})
return pd.DataFrame(performance)
# Evaluate weekly data
performance_weekly = {}
for name, model_list in models.items():
performance_weekly[name] = evaluate_models(train_weekly, test_weekly, model_list)
# Evaluate monthly data
performance_monthly = {}
for name, model_list in models.items():
performance_monthly[name] = evaluate_models(train_monthly, test_monthly, model_list)
# Display results
display(pd.concat(performance_weekly.values()))
display(pd.concat(performance_monthly.values()))
I get an error like this:
AttributeError: 'str' object has no attribute 'value'
File <command-3594900232608958>, line 42
40 performance_weekly = {}
41 for name, model_list in models.items():
---> 42 performance_weekly[name] = evaluate_models(train_weekly, test_weekly, model_list)
44 # Evaluate monthly data
45 performance_monthly = {}
File /local_disk0/.ephemeral_nfs/cluster_libraries/python/lib/python3.11/site-packages/darts/models/forecasting/exponential_smoothing.py:123, in ExponentialSmoothing.fit(self, series)
118 if self.seasonal_periods is None and series.has_range_index:
119 seasonal_periods_param = 12
121 hw_model = hw.ExponentialSmoothing(
122 series.values(copy=False),
--> 123 trend=self.trend if self.trend is None else self.trend.value,
124 damped_trend=self.damped,
125 seasonal=self.seasonal if self.seasonal is None else self.seasonal.value,
126 seasonal_periods=seasonal_periods_param,
127 freq=series.freq if series.has_datetime_index else None,
128 dates=series.time_index if series.has_datetime_index else None,
129 **self.constructor_kwargs,
130 )
131 hw_results = hw_model.fit(**self.fit_kwargs)
132 self.model = hw_results
The context:
I do timeseries forecasting.
Is this because of the methodology I have in splitting the training and test dataset?
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