The wait is over! ๐ŸŽ‰

Before moving on to code ARCH models...๐Ÿ‘จโ€๐Ÿ’ป

I will share the notebook in #Python for ARIMA models! ๐Ÿ““

๐Ÿšจ Check the end of the thread, there's a present! ๐ŸŽ

#TimeSeries #DataScience #MachineLearning Image
First, the steps covered:

1๏ธโƒฃ Import data (in this case Google stock price) ๐Ÿ“š

2๏ธโƒฃ Format data ๐Ÿ”จ

3๏ธโƒฃ Visualise prices and returns ๐Ÿ”

4๏ธโƒฃ Estimate parameters p, d and q ๐Ÿ”ฌ
5๏ธโƒฃ Build the initial model ๐Ÿ› ๏ธ

6๏ธโƒฃ Find the optimal model ๐ŸŒŸ

7๏ธโƒฃ Forecast! ๐Ÿ”ฎ
Here it is! The notebook in #Kaggle so you can build your ARIMA model! ๐Ÿ““

๐Ÿ‘‡๐Ÿ‘‡๐Ÿ‘‡

kaggle.com/code/davidandrโ€ฆ
There is a SECOND NOTEBOOK for ARIMA, this time for the auto-ARIMA model.

๐Ÿšจ Do this:

โ€ข ๐Ÿ””Follow me
โ€ข ๐Ÿ’ฌcomment
โ€ข ๐Ÿ”retweet

and I will send it to you!! ๐Ÿšจ

โ€ข โ€ข โ€ข

Missing some Tweet in this thread? You can try to force a refresh
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More from @daansan_ml

Nov 29
Answer these 8๏ธโƒฃ questions before starting any Time Series project!

๐Ÿ‘‡๐Ÿ‘‡๐Ÿ‘‡๐Ÿ‘‡

#DataScience #MachineLearning #Python Image
1๏ธโƒฃ What are your inputs and outputs to forecast?

๐Ÿ“ฅ Inputs are the historical data you provide to the model

๐Ÿ“ค Outputs are the predictions or forecasts for a future timestep
2๏ธโƒฃ What are your endogenous or exogenous input variables?

โ€ข Endogenous: are influenced by other variables within the system

โ€ข Exogenous: are not and can be considered outside the system

E.g., endogenous could be the number of daily purchases and exogenous the bank holidays.
Read 10 tweets
Nov 28
Build your ARCH model to predict volatility! ๐Ÿ”ฎ

๐Ÿงต ๐Ÿ‘‡

#TimeSeries #MachineLearning #Python #DataScience Image
First, you need to import the required libraries. Image
Now it is time to download the stock data (S&P500) and format it appropriately.

We need to set the frequency to Business days and the index as Datetime. Image
Read 9 tweets
Nov 21
Having missing values is a big problem in our time series analysis.

Learn how to deal with it! ๐Ÿ‘‡

๐Ÿงต

#DataScience #MachineLearning #Python
How to check if we have any missing values?

First, we can do a quick visual inspection. We can see that the line is not continuous at some points, which indicates the presence of missing values! โ˜ ๏ธ
The best scenario is that we don't really have missing values, but we just have the wrong frequency.

For example for stock data, we may be missing values on weekends. This can just be fixed by setting the frequency to business days or "B".
Read 11 tweets
Nov 20
5 great courses to learn Time Series Analysis and Forecasting in #Python

๐Ÿงต๐Ÿ‘‡๐Ÿ‘‡๐Ÿ‘‡

#DataScience #MachineLearning
1๏ธโƒฃ Time Series Analysis with Python in @365datascience

365datascience.com/courses/time-sโ€ฆ
2๏ธโƒฃ Forecasting Models and Time Series for Business in Python in @udemy

udemy.com/course/forecasโ€ฆ
Read 7 tweets
Nov 19
Decompose your Time Series data like this in #Python ๐Ÿ‘‡๐Ÿ‘‡ ๐Ÿ“ท

Check an example (check the notebook at the end ๐Ÿ‘จโ€๐Ÿ’ป) in this ๐Ÿงต

#DataScience #MachineLearning #timeseries
We can decompose a Time Series using seasonal_decompose from the "statsmodels" library in #Python.

I will show the process with a dataset that includes daily temperatures in London.
First, remember that there are two kinds of relationships between the elements of the time series:

1๏ธโƒฃ Additive:
y(t) = Trend + Seasonality + Residual

2๏ธโƒฃ Multiplicative:
y(t) = Trend x Seasonality x Residual
Read 9 tweets
Nov 18
What if I told you that you can build an ARIMA or SARIMA model automatically? ๐Ÿคฏ

๐Ÿ‘‡๐Ÿ‘‡๐Ÿ‘‡

#TimeSeries #Python #DataScience #MachineLearning
So far we've seen how to estimate the parameters and how to get the optimal model. โœจ

But this could be done faster with auto ARIMA! ๐Ÿคฆโ€โ™‚๏ธ

I'll show you how ๐Ÿ‘‡
First, import all libraries ๐Ÿ“š Image
Read 11 tweets

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