<jupyter_start><jupyter_text>Convert the date column which is currently of type "char" to an actual Date type:<jupyter_code>%%R
library(xts)
df$Date = as.yearmon(df$Date, format="%Y-%m") 
str(df)
%%R
dft = ts(df$Qty,frequency=12,start=c(2013,1)) # or dft = zoo(df$qty, order.by=df$date, frequency=12)
print(dft)
%%R
library(forecast)
hwm = HoltWinters(dft, gamma=TRUE)
hwf = forecast.HoltWinters(hwm, h=24)
summary(hwf)
%%R
plot.forecast(hwf)<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.1694915254237288, 'fraction_numerical': 0.023305084745762712, 'mean_word_length': 8.096153846153847, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 5, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '250080', 'license': 'MIT', 'n_tokens_mistral': 214, 'n_tokens_neox': 203, 'n_words': 40, 'path': 'jupyter_notebooks/rpy2/Forecasting_With_CSV_Data.ipynb', 'repo_name': 'manual123/Nacho-Jupyter-Notebooks', 'size': '472'}