Is there a way to predict future values of a column based on its values on a monthly basis till date? How do we get the values for the next say 6 months?
statsmodels.tsa.arima_model.ARIMA
class statsmodels.tsa.arima_model.ARIMA(endog, order, exog=None, dates=None, freq=None, missing='none')
Autoregressive Integrated Moving Average ARIMA(p,d,q) Model
You need more than pandas. I will introduce statsmodels library for statistics. (Especially ARIMA, please check one of these examples.) statsmodels works well with pandas.
You should use .predict() method of it for prediction.
Related
I am trying to predict churn and for this my dependent variable is a binary variable. The independent variables can be categorical, integer or timeseries data. I am in the feature selection mode and will like to know if I am running correlation, should I run correlation on time series data or not. If I do use a wrapper method and use a ML algorithm for such a problem, do I use models like ARIMA that are more suited for time series analysis or a decision tree model?
I have tried using Spearman correlation but am not finding any significant correlated dependent variables
You most likely should! Since churn rate may be affected by macroeconomical issues that will show in your autocorrelation function. I suggest paying a visit to statsmodel and making sure you understand ACF plots and PACF plots (that can be done with statsmodel quite easily) together with ARIMA models so you can do some fine tuning. As for the feature selection, you can try using an overfitted neural network or model with L1 regularization.
https://www.statsmodels.org/stable/index.html
I have to develop a Prediction Model using Python to predict if a site will crash next month or not depending on the occurances in the last 6 monthes. Input Parameters are: Environment(Dev,Prod,Test), Region(NA,APAC,EMEA) and the Date of the month.
I am using matplotlib, pandas and numpy. It will be a 2D Data Frame or a 3D Panel in Pandas. I am not sure as input parameters are 3 - Region, Env and Date.
I think below Machine Learning Algorithm should be used:
from sklearn.linear_model import LinearRegression
Please correct me if I am wrong.
Linear regression is fine, but calling it is just two line of work, i would suggest try multiple machine learning algorithms, tuning their hyperparameters and checking which gives the best performance, moreover you should look into feature engineering, maybe you could extract features from the already given data
Assume we have a time-series data that contains the daily orders count of last two years:
We can predict the future's orders using Python's statsmodels library:
fit = statsmodels.api.tsa.statespace.SARIMAX(
train.Count, order=(2, 1, 4),seasonal_order=(0,1,1,7)
).fit()
y_hat_avg['SARIMA'] = fit1.predict(
start="2018-06-16", end="2018-08-14", dynamic=True
)
Result (don't mind the numbers):
Now assume that our input data has some unusual increase or decrease, because of holidays or promotions in the company. So we added two columns that tell if each day was a "holiday" and a day that the company has had "promotion".
Is there a method (and a way of implementing it in Python) to use this new type of input data and help the model to understand the reason of outliers, and also predict the future's orders with providing "holiday" and "promotion_day" information?
fit1.predict('2018-08-29', holiday=True, is_promotion=False)
# or
fit1.predict(start="2018-08-20", end="2018-08-25", holiday=[0,0,0,1,1,0], is_promotion=[0,0,1,1,0,1])
SARIMAX, as a generalisation of the SARIMA model, is designed to handle exactly this. From the docs,
Parameters:
endog (array_like) – The observed time-series process y;
exog (array_like, optional) – Array of exogenous regressors, shaped (nobs, k).
You could pass the holiday and promotion_day as an array of size (nobs, 2) to exog, which will inform the model of the exogenous nature of some of these observations.
This problem have different names such as anomaly detection, rare event detection and extreme event detection.
There is some blog post at Uber engineering blog that may useful for understanding the problem and solution. Please look at here and here.
Although it's not from statsmodels, you can use facebook's prophet library for time series forecasting where you can pass dates with recurring events to your model.
See here.
Try this (it may or may not work based on your problem/data):
You can split your date into multiple features like day of week, day of month, month of year, year, is it last day in month?, is it first day in month? and many more if you think of it and then use some normal ML algorithm like Random Forests or Gradient Boosting Trees or Neural Networks (specially with embedding layers for your categorical features e.g. day of week) to train your model.
I am building a churn forecast model using features such as 1 year worth lags, holidays, moving averages, day/day ratios, seasonality factor extracted from statsmodels etc. It is clearly not an additive series, the magnitude of holiday churn each year is greater than that in previous years.
My XGB model predicts daily churn quite accurately, but it fails on holidays miserably (the trenches are slightly better predicted as compared to peaks):
in my opinion the model is unable to capture the exponential nature of the series. here is how it looks like at present. is there a way i can capture the exponential nature of the series, by using any additional features or something?
Data:
I have time series data for different countries and factors, e.g. birth rate for "Afghanistan" for years from 1972 up until 2007 (source).
Goal:
Predict e.g. birth rate for 2008 and 2012
Question:
I am familiar with linear regressions, but need some help on how to work with time series data and predict future values.
Can you point me to examples or share code snippets?
Take a look at the statsmodels Time Series Analysis module. Time series models are often based around autocorrelation, and the module has the standard univariate (for individual time series) AR(p) and MA(p) models, as well as the combined version ARIMA that allows for unit roots. You'll also find multivariate (for various interrelated time series) VAR models.
And here's a time series tutorial for statistical analysis and forecasting using pandas and statsmodels.
you can use ARIMA model and VAR Model in R.
ARIMA: Auto Regressive Integrated Moving Average model
VAR: Vector Auto Regressive model
For ARIMA model: click here
For VAR model: click here
For one time series data, use ARIMA model, however, if multiple time series data are related to each other, use VAR model.