- Last 7 days
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hypothes.is hypothes.isHypothesis23
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ARIMA(0, 0, 0)x(0, 0, 0, 12) - AIC:969.5419650946665 ARIMA(0, 0, 0)x(0, 0, 1, 12) - AIC:799.0140026908043 ARIMA(0, 0, 0)x(0, 1, 0, 12) - AIC:701.7072455506197 ARIMA(0, 0, 0)x(0, 1, 1, 12) - AIC:568.3211239351035 ARIMA(0, 0, 0)x(1, 0, 0, 12) - AIC:708.2727189545345 ARIMA(0, 0, 0)x(1, 0, 1, 12) - AIC:660.9171130206936 ARIMA(0, 0, 0)x(1, 1, 0, 12) - AIC:596.1563221105039 ARIMA(0, 0, 0)x(1, 1, 1, 12) - AIC:571.8620221843147 ARIMA(0, 0, 1)x(0, 0, 0, 12) - AIC:888.4893265461405 ARIMA(0, 0, 1)x(0, 0, 1, 12) - AIC:754.7451219152275 ARIMA(0, 0, 1)x(0, 1, 0, 12) - AIC:695.0468020327725 ARIMA(0, 0, 1)x(0, 1, 1, 12) - AIC:563.3526496700842 ARIMA(0, 0, 1)x(1, 0, 0, 12) - AIC:708.3487691701486 ARIMA(0, 0, 1)x(1, 0, 1, 12) - AIC:655.8968840891383 ARIMA(0, 0, 1)x(1, 1, 0, 12) - AIC:598.1490374699148 ARIMA(0, 0, 1)x(1, 1, 1, 12) - AIC:566.3367865157978 ARIMA(0, 1, 0)x(0, 0, 0, 12) - AIC:769.1876196189784 ARIMA(0, 1, 0)x(0, 0, 1, 12) - AIC:681.4253047727481 ARIMA(0, 1, 0)x(0, 1, 0, 12) - AIC:740.3973501203114 ARIMA(0, 1, 0)x(0, 1, 1, 12) - AIC:606.0067883430007 ARIMA(0, 1, 0)x(1, 0, 0, 12) - AIC:688.9276375883021 ARIMA(0, 1, 0)x(1, 0, 1, 12) - AIC:683.2372837276466 ARIMA(0, 1, 0)x(1, 1, 0, 12) - AIC:637.9760649104885 ARIMA(0, 1, 0)x(1, 1, 1, 12) - AIC:607.9989487123431 ARIMA(0, 1, 1)x(0, 0, 0, 12) - AIC:717.0512101206406 ARIMA(0, 1, 1)x(0, 0, 1, 12) - AIC:636.373429528529 ARIMA(0, 1, 1)x(0, 1, 0, 12) - AIC:692.512410906277 ARIMA(0, 1, 1)x(0, 1, 1, 12) - AIC:559.6920424480529 ARIMA(0, 1, 1)x(1, 0, 0, 12) - AIC:650.5293595230056 ARIMA(0, 1, 1)x(1, 0, 1, 12) - AIC:638.1908637932411 ARIMA(0, 1, 1)x(1, 1, 0, 12) - AIC:594.940391452659 ARIMA(0, 1, 1)x(1, 1, 1, 12) - AIC:562.5484300875305 ARIMA(1, 0, 0)x(0, 0, 0, 12) - AIC:775.150570595756 ARIMA(1, 0, 0)x(0, 0, 1, 12) - AIC:688.1982167211085 ARIMA(1, 0, 0)x(0, 1, 0, 12) - AIC:702.425519762607 ARIMA(1, 0, 0)x(0, 1, 1, 12) - AIC:570.1689904036024 ARIMA(1, 0, 0)x(1, 0, 0, 12) - AIC:688.2931195730088 ARIMA(1, 0, 0)x(1, 0, 1, 12) - AIC:662.6749372683774 ARIMA(1, 0, 0)x(1, 1, 0, 12) - AIC:590.7883988000217 ARIMA(1, 0, 0)x(1, 1, 1, 12) - AIC:573.825547011459 ARIMA(1, 0, 1)x(0, 0, 0, 12) - AIC:725.2611476282008 ARIMA(1, 0, 1)x(0, 0, 1, 12) - AIC:644.4595774810737 ARIMA(1, 0, 1)x(0, 1, 0, 12) - AIC:696.6355146715679 ARIMA(1, 0, 1)x(0, 1, 1, 12) - AIC:565.337721591011 ARIMA(1, 0, 1)x(1, 0, 0, 12) - AIC:651.3742765976529 ARIMA(1, 0, 1)x(1, 0, 1, 12) - AIC:657.7255114881699 ARIMA(1, 0, 1)x(1, 1, 0, 12) - AIC:592.7702867201957 ARIMA(1, 0, 1)x(1, 1, 1, 12) - AIC:567.3861300859227 ARIMA(1, 1, 0)x(0, 0, 0, 12) - AIC:750.4532664961456 ARIMA(1, 1, 0)x(0, 0, 1, 12) - AIC:665.693748389872 ARIMA(1, 1, 0)x(0, 1, 0, 12) - AIC:720.7807876037391 ARIMA(1, 1, 0)x(0, 1, 1, 12) - AIC:588.6301637485213 ARIMA(1, 1, 0)x(1, 0, 0, 12) - AIC:665.7141239363682 ARIMA(1, 1, 0)x(1, 0, 1, 12) - AIC:667.6890275833365 ARIMA(1, 1, 0)x(1, 1, 0, 12) - AIC:611.4437482645567 ARIMA(1, 1, 0)x(1, 1, 1, 12) - AIC:590.6185673644065 ARIMA(1, 1, 1)x(0, 0, 0, 12) - AIC:717.3211552781574 ARIMA(1, 1, 1)x(0, 0, 1, 12) - AIC:636.7110296932944 ARIMA(1, 1, 1)x(0, 1, 0, 12) - AIC:693.1696490581699 ARIMA(1, 1, 1)x(0, 1, 1, 12) - AIC:561.5301944999834 ARIMA(1, 1, 1)x(1, 0, 0, 12) - AIC:643.9735168529521 ARIMA(1, 1, 1)x(1, 0, 1, 12) - AIC:638.640931561371 ARIMA(1, 1, 1)x(1, 1, 0, 12) - AIC:588.5992832053371 ARIMA(1, 1, 1)x(1, 1, 1, 12) - AIC:564.5468753697722
This should not be shown.
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Summary of SARIMAX Print the summary which includes AIC
Why all these other headings? This is still part of the above?
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============================================================================== coef std err z P>|z| [0.025 0.975] ------------------------------------------------------------------------------ ar.L1 0.0483 0.306 0.158 0.875 -0.551 0.648 ma.L1 -1.0000 924.523 -0.001 0.999 -1813.031 1811.031 ma.S.L12 -1.0000 2355.498 -0.000 1.000 -4617.692 4615.692 sigma2 134.1503 3.35e+05 0.000 1.000 -6.57e+05 6.57e+05 ==============================================================================
Yup, that won't mean a thing to most readers (myself included) unless you explain it.
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Rigorous validation is paramount to establishing the model’s reliability and practical application. To ensure the model’s generalizability, we will employ a train-test split.
Why is this just being mentioned after all of the ML stuff has happened?
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The AIC value is: 561.5301944999834
Which tells us what?
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Start date of the data: 2015-01-31 00:00:00 End date of the data: 2022-12-31 00:00:00
??
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To facilitate c
The above graphic is a figure. Treat it as such!
Also, use dashed lines for one of the entries so that we can see that they are actually perfectly overlapping and not just take your word for it.
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Plot Diag
reference the figure correctly and explain it in the text!
Also, give it an actual meaningful caption.
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The Mean Squared Error of our forecasts is 1.41
units? What do you conclude from this?
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Forecasting Future Values As we conclude our modeling process, we generate predictions for the next 7 data points: Model Information: The result variable contains our fitted model’s details. Forecasting Method: We use the .get_forecast() method on our model results. Prediction Generation: This method analyzes observed patterns in our data to project future values. Output: We obtain forecasts for the next 7 time points, representing predicted air quality levels. This step transforms our analytical work into actionable insights for air quality management.
I don't understand what you are trying to say or do here. You have already done some of this above (I think) so I'd guess this is a summary, except that some of this I'm pretty sure I haven't seen?
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Interpreting the Forecast Plot
unnecessary
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Our plot
Reference the figure number! And stick a caption on it!
Is this plot for portland? That isn't apparent anywhere that I can see either.
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Represents the actual, historical air quality measurements Provides a baseline for comparing our predictions Forecasted Values (Orange Line) Depicts the future air quality levels predicted by our SARIMAX Time Series Model Allows us to visualize potential trends and patterns in air quality Confidence Interval (Shaded Region) The shaded area around the forecast line represents the 95% Confidence Interval (CI) Indicates the range within which we can be 95% confident that the true future values will fall Wider intervals suggest greater uncertainty in the prediction
This is a publication. Use complete sentences.
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we have landed on these specific recommendations.
Ok, let me just say that at this point, after reading through all your above analysis, I have NO IDEA what your recommendations are going to be. Which probably tells me that you did a poor job of actually showcasing your proof for each of these recommendations.
I haven't read what they are yet, but for every recommendation you make, I should be able to go back to a specific section or figure and see the exact reason for why you would make that prediction. If that is not the case, then you are either making unfounded recommendations, or you are not communicating what your analysis was for clearly enough.
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exasperated by the dry heat and lack of rainfall
Is this the actual cause? You showed some seasonality, I'm not sure these causes were showcased.
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As climate change raises temperatures and water sources dry up, wildfire season will continue to get worse over time.
Agreed, how would you interpret your data in that light? Can you see evidence of that? Is the effect more pronounced in cities near lots of national forest? Otherwise you are just conjecturing.
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Weather conditions Wind speed and direction Temperature fluctuations Humidity levels Atmospheric pressure Solar radiation intensity
Significantly affected? I thought you only saw a few of these at best as being significant contributors.
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That leaves us with three criteria gasses and all particulate matter.
But again, these are just part of the definition of AQI aren't they? So of course they have a large impact?
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Industrial manufacturing processes and agriculture are significant polluters of the environment. We should invest in the research of more environmentally friendly manufacturing methods, working with materials that require less combustion, or are recyclable.
Agreed, but I'm not sure you could see from your research if this was what was playing a large role?
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Bibliography
Look up how to properly cite websites according to APA rules
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Algorithm Dependence. This is the reliability of forecasts which are inherently tied to the chosen predictive algorithms. Different models may yield varying results, emphasizing the importance of algorithm selection and validation.
So how did you choose your algorithms with this in mind?
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The largest source of carbon monoxide, nitrogen dioxide, and ozone is the cars, trucks, and other vehicles we use daily (Environmental Protection Agency). We can lower our reliance on personal vehicles by utilizing public transportation, carpooling, walking, biking, increasing work from home to lower commutes when available, and overall be more considerate about if driving a car is necessary.
Did you see evidence of this? You had bus data. Did cities with less traffic show decreases in these values?
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The impact of the geological features can be seen in the image below
In what way?? EXPLAIN
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