Recently, I started working on time-series models and would mention that I am very new to python and ML as a whole.
I tried to implement a time-series model on wind speed data. Being a newbie, I followed the steps given in this article: https://kanoki.org/2020/04/30/time-series-analysis-and-forecasting-with-arima-python/ and it's a great one to start with but somehow I am unable to forecast my data (or I would say the forecasted data is constant around the 5.88 value!)
# FORECAST
n=39
forecast,err,ci = results.forecast(steps=n, alpha= 0.05)
windspeed_forecast = pd.DataFrame({'forecast':forecast},index=pd.date_range(start='22/8/2020 01:00:00', periods=n, freq='MS'))
windspeed_forecast
#plot for forecast
ax = windspeed[19:].SPEED.plot(label='observed', figsize=(20, 15))
windspeed_forecast.plot(ax=ax,label='Forecast',color='r')
ax.fill_between(windspeed_forecast.index, ci[:,0], ci[:,1], color='b', alpha=.005)
ax.set_xlabel('DATE')
ax.set_ylabel('SPEED')
plt.legend()
plt.show()
-What I think is the high AIC value of 700 might be the problem!
-Also, I am unable to figure out how can I create the column of Date-time for the forecasted values same as that of the original data(i.e. hourly based data of a specific date) [As shown in the ss number 1 and as shown in ss below - I need a column starting from 22/8/2020 with hourly gaps and so on].
Also, PFA the ss of my data in jupyter notebook (out of 191 total data, 152 used as train data and rest as test data)
Any suggestion/help regarding the same will be appreciated :)