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PREDICTION OF CRUDE OIL PRICES USING SVR WITH GRID SEARCH CROSS VALIDATION ALGORITHM
Abstract:
Crude
oil is the world's leading fuel, and its prices have a big impact on the global
environment, economy as well as oil exploration and exploitation activities.
Oil price forecasts are very useful to industries, governments and individuals.
Although many methods have been developed for predicting oil prices, it remains
one of the most challenging forecasting problems due to the high volatility of
oil prices
forecasting models that predict future events are used in numerous fields such
as economics and science because they are useful tools in decision making.
A perfect forecast provides insight
into the implications of an action or inaction and serves as a metric to judge
one’s ability to influence future events;The world's environment is affected by
the oil price falling. With the drop of oil prices, the fuel bills are lowered.
As a result, consumers are very likely to use more oil and thus increase the
carbon emission. In addition, there is less incentive to develop renewable and
clean energy resources. On the other hand, sustained low oil prices could lead
to a drop in global oil and gas exploration and exploitation activities.
Fluctuating oil prices also play an
important role in the global economy The fall in oil prices would result in a
modest boost to global economic activity, although the owners of oil sectors
suffer income losses. Recent research from the World B
an shows that for every 30% decline
of oil prices, the global GDP (Gross Domestic Product) would be increased by
0.5%. At the same time, the drop of oil prices would reduce the cost of living,
and hence the inflation rate would fall. So there is a chance of prediction of
the Proper and most approximate prediction in order to fix the situation if any
occurs.
Existing
System:
The prediction of the crude oil
rates based on the previous datasets on the data and prices as the feature _list
are inputs and and target list are predicted values. The implementation was on
the Linear Regression Model which is feasible to some extend for the prediction
of the crude oil prices. The implementation is on predicting the crude oil
prices for the days using Linear Regression Python Machine Algorithm and
plotting the graph based on the prediction.
Disadvantages:
Using Linear Regression algorithm
gives less approximate prediction compared to SVR Algorithm in the proposed
model in the project. As well the feature_list and target_list fitted into the
algorithm gives less predicting prices compared to the SVR, Comparatively
Linear regression performs poorly when there are non-linear relationships. They
are not naturally flexible enough to capture more complex patterns, and adding
the right interaction terms or polynomials can be tricky and time-consuming.
Proposed
System:
We
have implemented SVR algorithm(Support Vector Regression) of Machine learning
using Python. The predictions are most approximate with SVR Algorithms as they Linear
or Gaussian. The algorithm automatically uses the kernel function that is most
appropriate to the data.SVM uses the linear kernel when there are many
attributes (more than 100) in the training data, otherwise it uses the Gaussian
kernel. In the proposed system we have takes taken the datasets which has the
price and days based on the dataset we have made feature list and target list
where the target_list is price values andfeature list is the days. After the
analysis of data is done we have fitted both feature list and target list using
Python Machine learning SVN Algorithm and predicted the values for 1,30 and 365
days from the last day of the dataset values. Finally we have plotted a graph
based on the results from the predicted analysis done with SVN Algorithm.
Project Architecture:
Advantages:
SVMs
are a new promising non-linear, non-parametric classification technique, which
already showed good results in the medical diagnostics, optical character
recognition, electric load forecasting and other fields. Applied to solvency
analysis, the common objective of all these ,It has a regularization parameter,
which makes the user think about avoiding over-fitting. Secondly it uses the
kernel trick, so you can build in expert knowledge about the problem via
engineering the kernel. Thirdly an SVM is defined by a convex optimization problem
(no local minima) for which there is efficient methods (e.g. SMO). Lastly, it
is an approximation to a bound on the test error rate, and there is a
substantial body of theory behind it which suggests it should be a good idea.
The results that are generated by this algorithm gives more approximate and
accurate calculations of the price prediction value compared to the other
prediction algorithm for the dataset provided.
Hardware Requirements:
• RAM:
4GB and Higher
• Processor: Intel i3 and above
• Hard Disk: 500GB: Minimum
Software Requirements:
• OS: Windows
• Python IDE: python 2.7.x and above
• Pycharm IDE
• Setup tools and pip to be installed
for 3.6.x and above
thank you for your comment
pls call me on 8125424511