Scoring guide (Rubric) Some Rubric Criteria Points Define the problem and perform Exploratory Data Analysis Read the data as an appropriate time series data Plot the data Perform EDA Perform Decomposition 9 Data Pre processing Missing value treatment Visualize the processed data Train test split 4 Model Building Original Data Build forecasting models Linear regression Simple Average Moving Average Exponential Models (Single, Double, Triple) Check the performance of the models built 15 Check for Stationarity Check for stationarity Make the data stationary (if needed) 4 Model Building Stationary Data Generate ACF PACF Plot and find the AR, MA values Build different ARIMA models Auto ARIMA Manual ARIMA Build different SARIMA models Auto SARIMA Manual SARIMA Check the performance of the models built 12 Compare the performance of the models Compare the performance of all the models built Choose the best model with proper rationale Rebuild the best model using the entire data Make a forecast for the next 12 months 6 Actionable Insights Recommendations Conclude with the key takeaways (actionable insights and recommendations) for the business 4 Time Series Forecasting Project Problem FAQs How to treat the null values present in the data Is it required to use multiple methods to treat the null values for the model building procedures Ans Any method can be used to treat the missing values or impute the null values Please refer to the materials of Week 1 of the mentored learning session It is not imperative to use multiple imputation techniques to impute the null values Models like SARIMA and ARIMA is taking very long to execute Is there any particular way to make sure that the models run faster Ans There is no particular methodology in general which we can apply to make sure that the algorithms are executed a bit faster in the computer system Is it absolutely necessary to build both ARIMA and SARIMA models for this particular problem Ans It is necessary to build both ARIMA and SARIMA modelsand proper explanations should be provided for if any one is better than the other and whether any of the one would have made sense in the analysis Should two different business reports be created for the project along with two separate Python files Ans It is entirely up to the student Two different business reports accompanied by two different Python Notebooks can be submitted What are the expectations for the question which asks for a comment on the final model Ans For this particular question, it is expected that the model should be explained in terms of business terminology It should be clearly pointed out the reason for choosing this as the final model and how will the company be benefitted if they adopt this particular model for future sales Is it necessary that all three kinds of Exponential Smoothing models should be built for this assignment Ans It is mandatory to build all three exponential smoothing models Also it needs to be stated that out of all of them which works the best in this situation should also be stated For forecasting into the future, should only ARIMA SARIMA models be considered or should all the models be considered Ans All the models built till the end of the assignment should be considered Can we merge these two datasets in a common data frame and perform the project Ans No, the assignment needs to be solved differently for these two different data sets Do not merge these two data sets into one common data frame Is there a need to compare and contrast the results of the two datasets (Rose and Sparkling) Ans There is no need to compare the results of the models built on two different data sets Do we need to compute train RMSE as well for all the forecasting models Ans There is no requirement to compute the train RMSE , but putting the test RMSE in a tabular form is must Is there a need to build AR MA models separately and compute test RMSE for them as well Ans No you can choose to skip building AR MA models Please help me with the Time Series Forecasting project with the full Python code step by step for the given questions I am attaching the supporting data file to the drive Drive link https drive google com drive u 3 folders 1J8bnTtzqmlSbPZSdt2TUxeA6aTAwEtf9

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Scoring guide (Rubric) - Some Rubric Criteria Points Define the problem and perform Exploratory Data Analysis - Read the data as an appropriate time series

Scoring guide (Rubric) - Some Rubric

Criteria	Points
Define the problem and perform Exploratory Data Analysis - Read the data as an appropriate time series data - Plot the data - Perform EDA - Perform Decomposition	9
Data Pre-processing - Missing value treatment - Visualize the processed data - Train-test split	4
Model Building - Original Data - Build forecasting models - Linear regression - Simple Average - Moving Average - Exponential Models (Single, Double, Triple) - Check the performance of the models built	15
Check for Stationarity - Check for stationarity - Make the data stationary (if needed)	4
Model Building - Stationary Data - Generate ACF & PACF Plot and find the AR, MA values. - Build different ARIMA models - Auto ARIMA - Manual ARIMA - Build different SARIMA models - Auto SARIMA - Manual SARIMA - Check the performance of the models built	12
Compare the performance of the models - Compare the performance of all the models built - Choose the best model with proper rationale - Rebuild the best model using the entire data - Make a forecast for the next 12 months	6
Actionable Insights & Recommendations - Conclude with the key takeaways (actionable insights and recommendations) for the business	4

Time Series Forecasting Project Problem - FAQs

How to treat the null values present in the data? Is it required to use multiple methods to treat the null values for the model building procedures? Ans: Any method can be used to treat the missing values or impute the null values. Please refer to the materials of Week 1 of the mentored learning session. It is not imperative to use multiple imputation techniques to impute the null values.
Models like SARIMA and ARIMA is taking very long to execute. Is there any particular way to make sure that the models run faster? Ans: There is no particular methodology in general which we can apply to make sure that the algorithms are executed a bit faster in the computer system.
Is it absolutely necessary to build both ARIMA and SARIMA models for this particular problem? Ans: It is necessary to build both ARIMA and SARIMA modelsand proper explanations should be provided for if any one is better than the other and whether any of the one would have made sense in the analysis.
Should two different business reports be created for the project along with two separate Python files? Ans: It is entirely up to the student. Two different business reports accompanied by two different Python Notebooks can be submitted.
What are the expectations for the question which asks for a comment on the final model? Ans: For this particular question, it is expected that the model should be explained in terms of business terminology. It should be clearly pointed out the reason for choosing this as the final model and how will the company be benefitted if they adopt this particular model for future sales.
Is it necessary that all three kinds of Exponential Smoothing models should be built for this assignment? Ans: It is mandatory to build all three exponential smoothing models . Also it needs to be stated that out of all of them which works the best in this situation should also be stated
For forecasting into the future, should only ARIMA/SARIMA models be considered or should all the models be considered? Ans: All the models built till the end of the assignment should be considered.
Can we merge these two datasets in a common data frame and perform the project? Ans: No, the assignment needs to be solved differently for these two different data sets. Do not merge these two data sets into one common data frame.
Is there a need to compare and contrast the results of the two datasets (Rose and Sparkling)? Ans: There is no need to compare the results of the models built on two different data sets.
Do we need to compute train RMSE as well for all the forecasting models ? Ans: There is no requirement to compute the train RMSE , but putting the test RMSE in a tabular form is must .
Is there a need to build AR / MA models separately and compute test RMSE for them as well? Ans:No you can choose to skip building AR / MA models