Analyzing Spot Prices for Kerosene-Type Jet Fuel in the U.S. Gulf Coast Area
Comparison of spot prices by area?
It appears that the data only contains information for the U.S. Gulf Coast area, so a comparison by area is not possible with this dataset.
Use duoarea column
The dataset only contains data for the 'RGC' area in the duoarea column, so a comparison by area is not possible.
The dataset only contains data for the 'RGC' area in the duoarea column, so a comparison by area is not possible.
Analyze price variations within the 'RGC' area
We successfully plotted the price variations of kerosene-type jet fuel in the RGC area over time. The graph shows the changes in price per gallon from 1990 to 2016, providing a clear visual representation of trends and fluctuations.
Focus between 2010 and 2015
We successfully filtered the jet fuel price data for the period between 2010 and 2015. The data was sorted by date to ensure it was monotonic before filtering. A line plot was created to visually represent the price variations of kerosene-type jet fuel in the U.S. Gulf Coast region during this period.
Summarize key insights in the general dataset
Key Insights from the Dataset
Count of Data Points: There are 8,573 data points in the dataset.
Average Price: The average spot price for kerosene-type jet fuel is approximately $1.52 per gallon.
Price Variability: Prices vary significantly, with a standard deviation of about $0.95.
Price Range: Prices range from a minimum of 0.28pergallontoamaximumof5.07 per gallon.
Quartiles:
25% of prices are below $0.62.
50% of prices (median) are below $1.39.
75% of prices are below $2.14.
Missing Data: There are missing data points for the 'area-name' column, as it contains no entries.
Use arima algorithm to predict prices in 2025
We'll prepare the data for ARIMA forecasting by handling missing values and ensuring the data is in a suitable format. Then, we'll apply the ARIMA model to predict jet fuel prices for 2025. Let's start by handling the missing data.
ARIMA Model Summary
Data Cleaning: Removed columns with all missing values and unnecessary columns. Ensured the date column is the index and is in datetime format.
Model Fit: Fitted an ARIMA(5,1,0) model to the cleaned data.
Forecast: Predicted jet fuel prices for the next 10 years with monthly frequency. Displayed the initial forecast results with a 95% confidence interval.
Warnings: Encountered warnings regarding the lack of frequency information in the date index, which was ignored for forecasting purposes.
Visualize forecasted prices against actual data
We successfully visualized the forecasted prices against the actual data for jet
Can you plot this ARIMA forecast on a new time series?
To plot the ARIMA forecast on a new time series, we'll first need to generate or specify the new series. Could you provide more details on the new time series or should we simulate one?