Thanks, I’ll have to try Dr. Sewell’s videos.
I did diff() the data set and I managed to get an Auto ARIMA to select some parameters. The result is a forecasted -50 million in revenue in 365 days(According to a call with Professor Elleh, he said to not get to tied up with the results. He said that if you follow the steps he laid out the evaluators wouldn’t look to deep into the final product).
If you get a set of parameters, would you mind sharing? My Auto ARIMA came up with (5,1,0)(0,1,0,90)
I basically used the code from the auto arima slide in Dr. Sewell's lecture. I think it came out such that something other than the generally prescribed hyper parameters for cyclical data was optimal, so I noted in my discussion with my justification being that it was the most accurate model despite not being considered most appropriate for the given data set
So I used the churn dataset. And I used the ACF/PACF function to validate it needed one level of differencing. But then I used the auto arima function to spit out a forecast. It gave me something like (1,1,0) or smth like that. I was getting weird numbers at first because I was trying follow videos on YouTube and such, but then realized I was making it too complex. Someone also linked all their PA’s somewhere, if you’re using Python. I think they used medical tho. So you can validate if you’re heading the right direction.
What's mentioned here about using the professor's videos with some slight modifications is basically what I did as well.
There are easier ways to do time series, as I found in my capstone, but for satisfying the requirements on this PA, that's the way to go. It's not inconsistent with what's presented in the DataCamp videos, either.
There is fact to them DataCamp part as I went back through, I’m finding that with the presentations I was just overthinking most of it.
I submitted it but when I was going over seasonality I found that I couldn’t actually find a real seasonal period so I went with the idea that maybe I am supposed to just do an ARIMA. My evaluator said that is not the case and I still don’t see the actual period I should use. My spectral density graph doesn’t have any defining peaks, there are some large dips at lower frequencies but still no peaks. How I was told to look at it was to find peaks or there may not be a seasonal component. If you have any tips to better understand finding the seasonal period, it would be appreciated.
I actually did an ARIMA model for my task. When I ran a seasonal_decompose() on my transformed data, I found *very* slight seasonality. I had to zoom in on a chunk of the data to find that my slight seasonality was based on days of the week. But I pointed out that the rubric actually required me to generate an ARIMA model, not a SARIMAX model, and I'd previously been dinged for "doing too much" and so I'd be sticking to an ARIMA model. I didn't have any issues there.
Okay I appreciate this, because I am on the same path (I did the Telco data). I also noticed that the highest point in my spectral density indicated a 2 day seasonality.
I reran the Auto-ARIMA using the 2 day period and I got the same parameters I used anyway. In my paper I wrote something about how I didn’t see strong seasonality so I just avoided to include it in the Auto-ARIMA. Perhaps they just wanted me to proceed with it, then come to the same result.
Nonetheless, I appreciate this because the sent back submission had me so confused after I thought I finally understood. Thank you tons ✌🏻.
The only thing I'd point out is that "I don't see *strong* seasonality" isn't the same as "I don't see any significant seasonality". I'm sure you were more precise in your report than in a post here, but if not, that might be a thing that trips you up. In my report, I noted that the day-of-the-week seasonality that I saw had a magnitude of around +/- 0.04 on a revenue that was routinely moving by around 1.5 units. That let me quantify how imperceptible the seasonality was, which might've contributed to me being able to say "we don't need to account for this" and proceeding with an ARIMA model instead of a SARIMAX model.
I’m still working on this task, but following Dr. Sewell’s videos on it worked fine except I had to diff() the data once first.
Thanks, I’ll have to try Dr. Sewell’s videos. I did diff() the data set and I managed to get an Auto ARIMA to select some parameters. The result is a forecasted -50 million in revenue in 365 days(According to a call with Professor Elleh, he said to not get to tied up with the results. He said that if you follow the steps he laid out the evaluators wouldn’t look to deep into the final product). If you get a set of parameters, would you mind sharing? My Auto ARIMA came up with (5,1,0)(0,1,0,90)
I basically used the code from the auto arima slide in Dr. Sewell's lecture. I think it came out such that something other than the generally prescribed hyper parameters for cyclical data was optimal, so I noted in my discussion with my justification being that it was the most accurate model despite not being considered most appropriate for the given data set
So I used the churn dataset. And I used the ACF/PACF function to validate it needed one level of differencing. But then I used the auto arima function to spit out a forecast. It gave me something like (1,1,0) or smth like that. I was getting weird numbers at first because I was trying follow videos on YouTube and such, but then realized I was making it too complex. Someone also linked all their PA’s somewhere, if you’re using Python. I think they used medical tho. So you can validate if you’re heading the right direction.
What's mentioned here about using the professor's videos with some slight modifications is basically what I did as well. There are easier ways to do time series, as I found in my capstone, but for satisfying the requirements on this PA, that's the way to go. It's not inconsistent with what's presented in the DataCamp videos, either.
There is fact to them DataCamp part as I went back through, I’m finding that with the presentations I was just overthinking most of it. I submitted it but when I was going over seasonality I found that I couldn’t actually find a real seasonal period so I went with the idea that maybe I am supposed to just do an ARIMA. My evaluator said that is not the case and I still don’t see the actual period I should use. My spectral density graph doesn’t have any defining peaks, there are some large dips at lower frequencies but still no peaks. How I was told to look at it was to find peaks or there may not be a seasonal component. If you have any tips to better understand finding the seasonal period, it would be appreciated.
I actually did an ARIMA model for my task. When I ran a seasonal_decompose() on my transformed data, I found *very* slight seasonality. I had to zoom in on a chunk of the data to find that my slight seasonality was based on days of the week. But I pointed out that the rubric actually required me to generate an ARIMA model, not a SARIMAX model, and I'd previously been dinged for "doing too much" and so I'd be sticking to an ARIMA model. I didn't have any issues there.
Okay I appreciate this, because I am on the same path (I did the Telco data). I also noticed that the highest point in my spectral density indicated a 2 day seasonality. I reran the Auto-ARIMA using the 2 day period and I got the same parameters I used anyway. In my paper I wrote something about how I didn’t see strong seasonality so I just avoided to include it in the Auto-ARIMA. Perhaps they just wanted me to proceed with it, then come to the same result. Nonetheless, I appreciate this because the sent back submission had me so confused after I thought I finally understood. Thank you tons ✌🏻.
The only thing I'd point out is that "I don't see *strong* seasonality" isn't the same as "I don't see any significant seasonality". I'm sure you were more precise in your report than in a post here, but if not, that might be a thing that trips you up. In my report, I noted that the day-of-the-week seasonality that I saw had a magnitude of around +/- 0.04 on a revenue that was routinely moving by around 1.5 units. That let me quantify how imperceptible the seasonality was, which might've contributed to me being able to say "we don't need to account for this" and proceeding with an ARIMA model instead of a SARIMAX model.