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What is multi-step time series forecasting
The taxi service (ride-hailing) industry is growing for the last couple of years and it is expected to grow in near future. Taxi drivers need to choose where to hang tight for passengers as they can get somebody quickly. Passengers also prefer a quick taxi service whenever needed. We all have faced problems with taxi booking requests, that sometimes cannot be fulfilled or the wait time for ride arrival is very long due to the unavailability of a nearby taxi. One should feel fortunate in the event that you get a taxi booked in one go.
Taxi demand prediction has become extremely important for taxi-hailing (and e-haling) companies to understand their demand and optimize their fleet management.
To handle such issues, we would be building a model based on users ride request dataset, which would contain attributes: ride booking time, pickup point, and drop point latitude-longitude. This model would forecast the demand, for a particular time in different areas of the city which would help the company optimize taxi concentration to fulfill users demand.
Ola Bikes are suffering losses and losing out from their competition due to their inability to fulfil the ride requests of many users. To tackle this problem, you are asked to predict demand for rides in a certain region and a given future time window. This would help them allocate drivers more intelligently to meet the ride requests from users.
You have to predict ride requests (demand forecast) for a particular latitude
and longitude for a requested future time window/duration.
Raw Data contains a `number` (unique for every user), ride request DateTime (IST time),
pickup and drop location latitude, and longitude.
Defining a Good Ride Request
Ola Management knows the task is not easy and very important for their business to grow.
Hence, their business team has provided you some guidelines to follow.
Predict Task to test your model
After model development, Ola has requested us to build a prediction pipeline for the deployment of the model. To test our prediction pipeline, they have provided us clean data (filtered rides requests data based on good ride definition conditions) of 2021-03-26 based on which they have requested us to predict/show initial hours rides request demand forecast for 2021-03-27.
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