The disclosed embodiments illustrate a method and a system for predicting future activities of a user on a social media platform. The method includes extracting a first time series of one or more historical activities performed by the user from a social media platform server. The method further includes receiving a second time series of one or more future events from a requestor-computing device. The method further includes determining a first set of forecast values and a second set of forecast values based on the first time series and/or the second time series, wherein the first set of forecast values is determined using an ARIMA technique, and the second set of forecast values is determined using a regression modelling technique. The method further includes predicting the future activities of the user based on the first set of forecast values and the second set of forecast values.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for predicting one or more future activities of a user on a social media platform, said method comprising: extracting, by one or more processors, a first time series of one or more historical activities performed by said user on said social media platform from a social media platform server; receiving, by said one or more processors, a second time series of one or more future events from a computing device; determining, by said one or more processors, a first set of forecast values pertaining to said one or more future activities based on said first time series, wherein said first set of forecast values is determined using a first forecasting technique; determining, by said one or more processors, a second set of forecast values pertaining to said one or more future activities based on said first time series and said second time series, wherein said second set of forecast values is determined using a second forecasting technique; predicting, by said one or more processors, said one or more future activities of said user based on said first set of forecast values and said second set of forecast values; and recommending, by said one or more processors, one or more products/services based on said predicted one or more future activities of said user. 2. The method of claim 1, wherein said one or more historical activities performed by said user comprise one or more of one or more messages posted, shared, or followed by said user during a first predefined time duration and one or more products or services liked or disliked by said user during said first predefined time duration. 3. The method of claim 1, wherein said one or more future events comprise one or more periodic events or one or more non-periodic events, wherein said one or more periodic events comprise one or more of a festival, a sport event, and an exam session, and wherein said one or more non-periodic events comprise one or more of a musical event, an election campaign, and a natural catastrophes. 4. The method of claim 1, wherein said first time series comprises at least a count of said one or more historical activities performed by said user during a first predefined time duration. 5. The method of claim 4, wherein a mean and a variance of said count of said one or more historical activities are constant during said first predefined time duration. 6. The method of claim 1, wherein said first forecasting technique corresponds to an auto regressive integrated moving average (ARIMA) technique, and wherein said second forecasting technique is based on a regression modelling technique. 7. The method of claim 1, wherein said one or more future activities may comprise one or more of a frequency of visit of said user to said social media platform during a second predefined time duration, a count of messages to be posted, shared, or followed by said user during said second predefined time duration, and a like or a dislike of said user towards a product or a service during said second predefined time duration. 8. The method of claim 7, wherein said one or more future activities of said user are predicted, by said one or more processors, based on an aggregation of at least said first set of forecast values and said second set of forecast values. 9. The method of claim 8, wherein said aggregation corresponds to an average or a weighted average of at least said first set of forecast values and said second set of forecast values. 10. A system for predicting one or more future activities of a user on a social media platform, said system comprising: one or more processors configured to: extract a first time series of one or more historical activities performed by said user on said social media platform during a first predefined time duration from a social media platform server; receive a second time series of one or more future events for a second predefined time duration from a requestor-computing device; determine a first set of forecast values pertaining to said one or more future activities based on said first time series, wherein said first set of forecast values is determined using a first forecasting technique; determine a second set of forecast values pertaining to said one or more future activities based on said first time series and said second time series, wherein said second set of forecast values is determined using a second forecasting technique; predict said one or more future activities of said user for said second predefined time duration based on said first set of forecast values and said second set of forecast values; and recommend one or more products/services based on said predicted one or more future activities of said user during said second predefined time duration. 11. The system of claim 10, wherein said one or more historical activities performed by said user comprise one or more of one or more messages posted, shared, or followed by said user during said first predefined time duration and one or more products or services liked or disliked by said user during said first predefined time duration. 12. The system of claim 10, wherein said one or more future events comprise one or more periodic events or one or more non-periodic events, wherein said one or more periodic events comprise one or more of a festival, a sport event, and an exam session, and wherein said one or more non-periodic events comprise one or more of a musical event, an election campaign, and a natural catastrophes. 13. The system of claim 10, wherein said first forecasting technique corresponds to an auto regressive integrated moving average (ARIMA) technique, and wherein said second forecasting technique is based on a regression modelling technique. 14. The system of claim 10, wherein said one or more future activities may comprise one or more of a frequency of visit of said user to said social media platform during said second predefined time duration, a count of messages to be posted, shared, or followed by said user during said second predefined time duration, and a like or a dislike of said user towards a product or a service during said second predefined time duration. 15. The system of claim 14, wherein said one or more processors are further configured to predict said one or more future activities of said user based on an aggregation of at least said first set of forecast values and said second set of forecast values. 16. The system of claim 15, wherein said aggregation corresponds to an average or a weighted average of at least said first set of forecast values and said second set of forecast values. 17. A computer program product for use with a computer, the computer program product comprising a non-transitory computer readable medium, wherein the non-transitory computer readable medium stores a computer program code for predicting one or more future activities of a user on a social media platform, wherein the computer program code is executable by one or more processors to: extract a first time series of one or more historical activities performed by said user on said social media platform during a first predefined time duration from a social media platform server; receive a second time series of one or more future events for a second predefined time duration from a requestor-computing device; determine a first set of forecast values pertaining to said one or more future activities based on said first time series, wherein said first set of forecast values is determined using an auto regressive integrated moving average (ARIMA) technique; determine a second set of forecast values pertaining to said one or more future activities based on said first time series and said second time series, wherein said second set of forecast values is determined based on a regression modelling technique; predict said one or more future activities of said user for said second predefined time duration based on said first set of forecast values and said second set of forecast values; and recommend one or more products/services based on said predicted one or more future activities of said user during said second predefined time duration.