exponential smoothing in Pyspark

[NamrataRade]

Hello, I have a pandas code for exponential smoothening. But I am not able to do the same in pyspark.
def exponential_smoothing(x, alpha):
result = []
for value in x:
if result:
smoothed_value = alpha * value + (1 - alpha) * result[-1]
else:
smoothed_value = value
result.append(smoothed_value)
return result
def apply_exponential_smoothing(df, alpha):
df['product_area_sales_value_N_mean_T'] = df.groupby(['area_id', 'product_id'])['product_area_sales_value_N_mean'].transform(lambda x: exponential_smoothing(x, alpha))
df['product_area_sales_unit_N_mean_T'] = df.groupby(['area_id', 'product_id'])['product_area_sales_unit_N_mean'].transform(lambda x: exponential_smoothing(x, alpha))
return df

tmp3 = apply_exponential_smoothing(tmp3, alpha=0.8)
this is the code. here in pyspark, I am not able to fetch previous row smoothen value. there is no such functionality in pyspark. Please suggest solution in spark