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Forecasting ticket sales and city
Forecasting an individual based on a representative groupBest regression model to use for sales predictionRegression (and Forecasting) on RentSales Prediction for Fashion Retail DataArtificially Increasing Training dataTime series prediction of discontinuous dataForecast vs Prediction: What is the difference?How to decide which forecasting model to use?Forecasting energy consumption with no historical data when there is a trend
$begingroup$
I am learning data science. I have the following dataset for train tickets:
1. order_date_meduim
order,date,medium
95062,2017-09-11,35
171081,2017-07-05,39
122867,2017-08-18,39
107186,2017-11-23,
171085,2017-09-02,
2. order_ordercityA_ordercityB [some order has only 1 ordercity, I think ordercity means here which city is something like source and destination]
order,ordercityA,ordercityB
81773,4,11
105838,4,
76153,24,18
93058,12,
11623,24,3
3070,24,3
3. order_ticketcount,ticketclass
order,ticketcount,ticketclass
246783,1,pax
1693998,2,pax
1958576,1,other
673681,1,pax
1593899,1,pax
194035,1,pax
I need to forecast the ticket sales for a week and also the ordercity with medium of booking.
As I am new, could someone give a possible answer about how to create a prediction model that could predict the sales for 1 week? Also, I doubt the data is time-series data.
I code in Python.
python predictive-modeling prediction data-science-model
asked Mar 9 at 23:27
Roshan MehtaRoshan Mehta
666
$endgroup$
This question has an open bounty worth +50
reputation from Roshan Mehta ending ending at 2019-03-19 07:15:53Z">in 2 days.
This question has not received enough attention.
add a comment |
$begingroup$
I am learning data science. I have the following dataset for train tickets:
1. order_date_meduim
order,date,medium
95062,2017-09-11,35
171081,2017-07-05,39
122867,2017-08-18,39
107186,2017-11-23,
171085,2017-09-02,
2. order_ordercityA_ordercityB [some order has only 1 ordercity, I think ordercity means here which city is something like source and destination]
order,ordercityA,ordercityB
81773,4,11
105838,4,
76153,24,18
93058,12,
11623,24,3
3070,24,3
3. order_ticketcount,ticketclass
order,ticketcount,ticketclass
246783,1,pax
1693998,2,pax
1958576,1,other
673681,1,pax
1593899,1,pax
194035,1,pax
I need to forecast the ticket sales for a week and also the ordercity with medium of booking.
As I am new, could someone give a possible answer about how to create a prediction model that could predict the sales for 1 week? Also, I doubt the data is time-series data.
I code in Python.
python predictive-modeling prediction data-science-model
asked Mar 9 at 23:27
Roshan MehtaRoshan Mehta
666
$endgroup$
This question has an open bounty worth +50
reputation from Roshan Mehta ending ending at 2019-03-19 07:15:53Z">in 2 days.
This question has not received enough attention.
$begingroup$
Yes it's timeseries; work on adding more features to the data which you think could help the model to strengthen its preds.add various statistics and groupby cols, check for holidays and all, weekends, hour etc, etc and I truly recommend not to jump into Time series first without having proper aspect of ML or hands on experience on various datasets/comps/hacks
$endgroup$
– Aditya
Mar 10 at 2:53
add a comment |
$begingroup$
I am learning data science. I have the following dataset for train tickets:
1. order_date_meduim
order,date,medium
95062,2017-09-11,35
171081,2017-07-05,39
122867,2017-08-18,39
107186,2017-11-23,
171085,2017-09-02,
2. order_ordercityA_ordercityB [some order has only 1 ordercity, I think ordercity means here which city is something like source and destination]
order,ordercityA,ordercityB
81773,4,11
105838,4,
76153,24,18
93058,12,
11623,24,3
3070,24,3
3. order_ticketcount,ticketclass
order,ticketcount,ticketclass
246783,1,pax
1693998,2,pax
1958576,1,other
673681,1,pax
1593899,1,pax
194035,1,pax
I need to forecast the ticket sales for a week and also the ordercity with medium of booking.
As I am new, could someone give a possible answer about how to create a prediction model that could predict the sales for 1 week? Also, I doubt the data is time-series data.
I code in Python.
python predictive-modeling prediction data-science-model
asked Mar 9 at 23:27
Roshan MehtaRoshan Mehta
666
$endgroup$
I am learning data science. I have the following dataset for train tickets:
1. order_date_meduim
order,date,medium
95062,2017-09-11,35
171081,2017-07-05,39
122867,2017-08-18,39
107186,2017-11-23,
171085,2017-09-02,
2. order_ordercityA_ordercityB [some order has only 1 ordercity, I think ordercity means here which city is something like source and destination]
order,ordercityA,ordercityB
81773,4,11
105838,4,
76153,24,18
93058,12,
11623,24,3
3070,24,3
3. order_ticketcount,ticketclass
order,ticketcount,ticketclass
246783,1,pax
1693998,2,pax
1958576,1,other
673681,1,pax
1593899,1,pax
194035,1,pax
I need to forecast the ticket sales for a week and also the ordercity with medium of booking.
As I am new, could someone give a possible answer about how to create a prediction model that could predict the sales for 1 week? Also, I doubt the data is time-series data.
I code in Python.
python predictive-modeling prediction data-science-model
python predictive-modeling prediction data-science-model
asked Mar 9 at 23:27
Roshan MehtaRoshan Mehta
666
asked Mar 9 at 23:27
Roshan MehtaRoshan Mehta
666
edited Mar 9 at 23:40
Roshan Mehta
asked Mar 9 at 23:27
Roshan MehtaRoshan Mehta
666
asked Mar 9 at 23:27
Roshan MehtaRoshan Mehta
666
asked Mar 9 at 23:27
Roshan MehtaRoshan Mehta
666
666
This question has an open bounty worth +50
reputation from Roshan Mehta ending ending at 2019-03-19 07:15:53Z">in 2 days.
This question has not received enough attention.
This question has an open bounty worth +50
reputation from Roshan Mehta ending ending at 2019-03-19 07:15:53Z">in 2 days.
This question has not received enough attention.
$begingroup$
Yes it's timeseries; work on adding more features to the data which you think could help the model to strengthen its preds.add various statistics and groupby cols, check for holidays and all, weekends, hour etc, etc and I truly recommend not to jump into Time series first without having proper aspect of ML or hands on experience on various datasets/comps/hacks
$endgroup$
– Aditya
Mar 10 at 2:53
add a comment |
$begingroup$
Yes it's timeseries; work on adding more features to the data which you think could help the model to strengthen its preds.add various statistics and groupby cols, check for holidays and all, weekends, hour etc, etc and I truly recommend not to jump into Time series first without having proper aspect of ML or hands on experience on various datasets/comps/hacks
$endgroup$
– Aditya
Mar 10 at 2:53
$begingroup$
Yes it's timeseries; work on adding more features to the data which you think could help the model to strengthen its preds.add various statistics and groupby cols, check for holidays and all, weekends, hour etc, etc and I truly recommend not to jump into Time series first without having proper aspect of ML or hands on experience on various datasets/comps/hacks
$endgroup$
– Aditya
Mar 10 at 2:53
$begingroup$
Yes it's timeseries; work on adding more features to the data which you think could help the model to strengthen its preds.add various statistics and groupby cols, check for holidays and all, weekends, hour etc, etc and I truly recommend not to jump into Time series first without having proper aspect of ML or hands on experience on various datasets/comps/hacks
$endgroup$
– Aditya
Mar 10 at 2:53
add a comment |
2 Answers
2
active
oldest
votes
$begingroup$
You have got yourself a time series forecasting problem. And with multiple input variables it is called multivariate time series forecasting.
What Is Time Series Forecasting?
You can start with EDA on your data and find out if you can see any trend or seasonality. ( You might need to add or update your current features to get underlying trend/seasonality )
After EDA, you can start looking into following models, all of them are the go-to for time series prediction problem:
- Classical, Statistical
- ARMA for stationary data
- ARIMA for data with a trend - Refer
- SARIMA for data with seasonality
- Holt-Winters Forecasting - Refer
- Theta method - Refer
- Fourier Transformation - Refer
- Machine Learning
- Quantile Regression Forest(QRF)
- Support Vector Regression(SVR)
- Recurrent Neural Networks(RNNs) (LSTM)
If you are not comfortable with Statistics then I would advise you to start with LSTMs for forecasting - Refer
answered yesterday
PreetPreet
3334
$endgroup$
$begingroup$
How to find out the ticketclass or so called categorical column using timeseries forecasting
$endgroup$
– Roshan Mehta
yesterday
$begingroup$
This is an exact duplicate of another answer you posted. Please customize the answer to the actual question. This is pretty broad as advice.
$endgroup$
– Sean Owen♦
2 hours ago
add a comment |
$begingroup$
Generally, the first step in modeling is merging all separate datasets into a single dataset. It looks like the data can be joined on order as a key.
Then, sort the data by date.
Next, visualize the data to see general trends and outliers.
The prophet package can estimate forecasts for time series data. A quick-start notebook is here.
answered Mar 12 at 17:05
Brian SpieringBrian Spiering
4,1131029
$endgroup$
$begingroup$
Which join, outer, inner or cartesian product?
$endgroup$
– Roshan Mehta
Mar 12 at 17:25
$begingroup$
Depends on how you want to handle duplicate and missing data. Here is more details jakevdp.github.io/PythonDataScienceHandbook/…
$endgroup$
– Brian Spiering
Mar 12 at 17:42
add a comment |
Your Answer
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2 Answers
2
active
oldest
votes
2 Answers
2
active
oldest
votes
active
oldest
votes
active
oldest
votes
$begingroup$
You have got yourself a time series forecasting problem. And with multiple input variables it is called multivariate time series forecasting.
What Is Time Series Forecasting?
You can start with EDA on your data and find out if you can see any trend or seasonality. ( You might need to add or update your current features to get underlying trend/seasonality )
After EDA, you can start looking into following models, all of them are the go-to for time series prediction problem:
- Classical, Statistical
- ARMA for stationary data
- ARIMA for data with a trend - Refer
- SARIMA for data with seasonality
- Holt-Winters Forecasting - Refer
- Theta method - Refer
- Fourier Transformation - Refer
- Machine Learning
- Quantile Regression Forest(QRF)
- Support Vector Regression(SVR)
- Recurrent Neural Networks(RNNs) (LSTM)
If you are not comfortable with Statistics then I would advise you to start with LSTMs for forecasting - Refer
answered yesterday
PreetPreet
3334
$endgroup$
$begingroup$
How to find out the ticketclass or so called categorical column using timeseries forecasting
$endgroup$
– Roshan Mehta
yesterday
$begingroup$
This is an exact duplicate of another answer you posted. Please customize the answer to the actual question. This is pretty broad as advice.
$endgroup$
– Sean Owen♦
2 hours ago
add a comment |
$begingroup$
You have got yourself a time series forecasting problem. And with multiple input variables it is called multivariate time series forecasting.
What Is Time Series Forecasting?
You can start with EDA on your data and find out if you can see any trend or seasonality. ( You might need to add or update your current features to get underlying trend/seasonality )
After EDA, you can start looking into following models, all of them are the go-to for time series prediction problem:
- Classical, Statistical
- ARMA for stationary data
- ARIMA for data with a trend - Refer
- SARIMA for data with seasonality
- Holt-Winters Forecasting - Refer
- Theta method - Refer
- Fourier Transformation - Refer
- Machine Learning
- Quantile Regression Forest(QRF)
- Support Vector Regression(SVR)
- Recurrent Neural Networks(RNNs) (LSTM)
If you are not comfortable with Statistics then I would advise you to start with LSTMs for forecasting - Refer
answered yesterday
PreetPreet
3334
$endgroup$
$begingroup$
How to find out the ticketclass or so called categorical column using timeseries forecasting
$endgroup$
– Roshan Mehta
yesterday
$begingroup$
This is an exact duplicate of another answer you posted. Please customize the answer to the actual question. This is pretty broad as advice.
$endgroup$
– Sean Owen♦
2 hours ago
add a comment |
$begingroup$
You have got yourself a time series forecasting problem. And with multiple input variables it is called multivariate time series forecasting.
What Is Time Series Forecasting?
You can start with EDA on your data and find out if you can see any trend or seasonality. ( You might need to add or update your current features to get underlying trend/seasonality )
After EDA, you can start looking into following models, all of them are the go-to for time series prediction problem:
- Classical, Statistical
- ARMA for stationary data
- ARIMA for data with a trend - Refer
- SARIMA for data with seasonality
- Holt-Winters Forecasting - Refer
- Theta method - Refer
- Fourier Transformation - Refer
- Machine Learning
- Quantile Regression Forest(QRF)
- Support Vector Regression(SVR)
- Recurrent Neural Networks(RNNs) (LSTM)
If you are not comfortable with Statistics then I would advise you to start with LSTMs for forecasting - Refer
answered yesterday
PreetPreet
3334
$endgroup$
You have got yourself a time series forecasting problem. And with multiple input variables it is called multivariate time series forecasting.
What Is Time Series Forecasting?
You can start with EDA on your data and find out if you can see any trend or seasonality. ( You might need to add or update your current features to get underlying trend/seasonality )
After EDA, you can start looking into following models, all of them are the go-to for time series prediction problem:
- Classical, Statistical
- ARMA for stationary data
- ARIMA for data with a trend - Refer
- SARIMA for data with seasonality
- Holt-Winters Forecasting - Refer
- Theta method - Refer
- Fourier Transformation - Refer
- Machine Learning
- Quantile Regression Forest(QRF)
- Support Vector Regression(SVR)
- Recurrent Neural Networks(RNNs) (LSTM)
If you are not comfortable with Statistics then I would advise you to start with LSTMs for forecasting - Refer
answered yesterday
PreetPreet
3334
answered yesterday
PreetPreet
3334
answered yesterday
PreetPreet
3334
answered yesterday
PreetPreet
3334
3334
$begingroup$
How to find out the ticketclass or so called categorical column using timeseries forecasting
$endgroup$
– Roshan Mehta
yesterday
$begingroup$
This is an exact duplicate of another answer you posted. Please customize the answer to the actual question. This is pretty broad as advice.
$endgroup$
– Sean Owen♦
2 hours ago
add a comment |
$begingroup$
How to find out the ticketclass or so called categorical column using timeseries forecasting
$endgroup$
– Roshan Mehta
yesterday
$begingroup$
This is an exact duplicate of another answer you posted. Please customize the answer to the actual question. This is pretty broad as advice.
$endgroup$
– Sean Owen♦
2 hours ago
$begingroup$
How to find out the ticketclass or so called categorical column using timeseries forecasting
$endgroup$
– Roshan Mehta
yesterday
$begingroup$
How to find out the ticketclass or so called categorical column using timeseries forecasting
$endgroup$
– Roshan Mehta
yesterday
$begingroup$
This is an exact duplicate of another answer you posted. Please customize the answer to the actual question. This is pretty broad as advice.
$endgroup$
– Sean Owen♦
2 hours ago
$begingroup$
This is an exact duplicate of another answer you posted. Please customize the answer to the actual question. This is pretty broad as advice.
$endgroup$
– Sean Owen♦
2 hours ago
add a comment |
$begingroup$
Generally, the first step in modeling is merging all separate datasets into a single dataset. It looks like the data can be joined on order as a key.
Then, sort the data by date.
Next, visualize the data to see general trends and outliers.
The prophet package can estimate forecasts for time series data. A quick-start notebook is here.
answered Mar 12 at 17:05
Brian SpieringBrian Spiering
4,1131029
$endgroup$
$begingroup$
Which join, outer, inner or cartesian product?
$endgroup$
– Roshan Mehta
Mar 12 at 17:25
$begingroup$
Depends on how you want to handle duplicate and missing data. Here is more details jakevdp.github.io/PythonDataScienceHandbook/…
$endgroup$
– Brian Spiering
Mar 12 at 17:42
add a comment |
$begingroup$
Generally, the first step in modeling is merging all separate datasets into a single dataset. It looks like the data can be joined on order as a key.
Then, sort the data by date.
Next, visualize the data to see general trends and outliers.
The prophet package can estimate forecasts for time series data. A quick-start notebook is here.
answered Mar 12 at 17:05
Brian SpieringBrian Spiering
4,1131029
$endgroup$
$begingroup$
Which join, outer, inner or cartesian product?
$endgroup$
– Roshan Mehta
Mar 12 at 17:25
$begingroup$
Depends on how you want to handle duplicate and missing data. Here is more details jakevdp.github.io/PythonDataScienceHandbook/…
$endgroup$
– Brian Spiering
Mar 12 at 17:42
add a comment |
$begingroup$
Generally, the first step in modeling is merging all separate datasets into a single dataset. It looks like the data can be joined on order as a key.
Then, sort the data by date.
Next, visualize the data to see general trends and outliers.
The prophet package can estimate forecasts for time series data. A quick-start notebook is here.
answered Mar 12 at 17:05
Brian SpieringBrian Spiering
4,1131029
$endgroup$
Generally, the first step in modeling is merging all separate datasets into a single dataset. It looks like the data can be joined on order as a key.
Then, sort the data by date.
Next, visualize the data to see general trends and outliers.
The prophet package can estimate forecasts for time series data. A quick-start notebook is here.
answered Mar 12 at 17:05
Brian SpieringBrian Spiering
4,1131029
edited Mar 12 at 17:43
answered Mar 12 at 17:05
Brian SpieringBrian Spiering
4,1131029
answered Mar 12 at 17:05
Brian SpieringBrian Spiering
4,1131029
answered Mar 12 at 17:05
Brian SpieringBrian Spiering
4,1131029
4,1131029
$begingroup$
Which join, outer, inner or cartesian product?
$endgroup$
– Roshan Mehta
Mar 12 at 17:25
$begingroup$
Depends on how you want to handle duplicate and missing data. Here is more details jakevdp.github.io/PythonDataScienceHandbook/…
$endgroup$
– Brian Spiering
Mar 12 at 17:42
add a comment |
$begingroup$
Which join, outer, inner or cartesian product?
$endgroup$
– Roshan Mehta
Mar 12 at 17:25
$begingroup$
Depends on how you want to handle duplicate and missing data. Here is more details jakevdp.github.io/PythonDataScienceHandbook/…
$endgroup$
– Brian Spiering
Mar 12 at 17:42
$begingroup$
Which join, outer, inner or cartesian product?
$endgroup$
– Roshan Mehta
Mar 12 at 17:25
$begingroup$
Which join, outer, inner or cartesian product?
$endgroup$
– Roshan Mehta
Mar 12 at 17:25
$begingroup$
Depends on how you want to handle duplicate and missing data. Here is more details jakevdp.github.io/PythonDataScienceHandbook/…
$endgroup$
– Brian Spiering
Mar 12 at 17:42
$begingroup$
Depends on how you want to handle duplicate and missing data. Here is more details jakevdp.github.io/PythonDataScienceHandbook/…
$endgroup$
– Brian Spiering
Mar 12 at 17:42
add a comment |
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$begingroup$
Yes it's timeseries; work on adding more features to the data which you think could help the model to strengthen its preds.add various statistics and groupby cols, check for holidays and all, weekends, hour etc, etc and I truly recommend not to jump into Time series first without having proper aspect of ML or hands on experience on various datasets/comps/hacks
$endgroup$
– Aditya
Mar 10 at 2:53