About applying time series forecasting to problems better suited for reinforcement learning, like toy example...
$begingroup$
"Jack's car rental" is an example of a reinforcement learning problem, proposed in the Sutton & Barto book, in which the goal is to optimize the daily distribution of cars in two locations of the company, so as to meet the customers demand.
Assuming that a large historic dataset has been collected, containing daily customer rental data in both locations, with these data showing a trend, seasonality and a reasonable daily variation, my question is:
How far from a good choice would be applying time series forecasting to predict the future rental demand, which would clearly support the management of the cars relocation, instead of applying reinforcement learning?
Let's assume that the dataset also includes additional time series like number of rental opportunities lost by lack of vehicles, movement of cars between both locations due to customer preferences, dynamics of the fleet due to buy/sell operations, etc.
Is this a problem that you would address by applying a time series forecasting approach?
time-series predictive-modeling lstm reinforcement-learning forecasting
asked 3 mins ago
datarieldatariel
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$begingroup$
"Jack's car rental" is an example of a reinforcement learning problem, proposed in the Sutton & Barto book, in which the goal is to optimize the daily distribution of cars in two locations of the company, so as to meet the customers demand.
Assuming that a large historic dataset has been collected, containing daily customer rental data in both locations, with these data showing a trend, seasonality and a reasonable daily variation, my question is:
How far from a good choice would be applying time series forecasting to predict the future rental demand, which would clearly support the management of the cars relocation, instead of applying reinforcement learning?
Let's assume that the dataset also includes additional time series like number of rental opportunities lost by lack of vehicles, movement of cars between both locations due to customer preferences, dynamics of the fleet due to buy/sell operations, etc.
Is this a problem that you would address by applying a time series forecasting approach?
time-series predictive-modeling lstm reinforcement-learning forecasting
asked 3 mins ago
datarieldatariel
11
New contributor
datariel is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
add a comment |
$begingroup$
"Jack's car rental" is an example of a reinforcement learning problem, proposed in the Sutton & Barto book, in which the goal is to optimize the daily distribution of cars in two locations of the company, so as to meet the customers demand.
Assuming that a large historic dataset has been collected, containing daily customer rental data in both locations, with these data showing a trend, seasonality and a reasonable daily variation, my question is:
How far from a good choice would be applying time series forecasting to predict the future rental demand, which would clearly support the management of the cars relocation, instead of applying reinforcement learning?
Let's assume that the dataset also includes additional time series like number of rental opportunities lost by lack of vehicles, movement of cars between both locations due to customer preferences, dynamics of the fleet due to buy/sell operations, etc.
Is this a problem that you would address by applying a time series forecasting approach?
time-series predictive-modeling lstm reinforcement-learning forecasting
asked 3 mins ago
datarieldatariel
11
New contributor
datariel is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
"Jack's car rental" is an example of a reinforcement learning problem, proposed in the Sutton & Barto book, in which the goal is to optimize the daily distribution of cars in two locations of the company, so as to meet the customers demand.
Assuming that a large historic dataset has been collected, containing daily customer rental data in both locations, with these data showing a trend, seasonality and a reasonable daily variation, my question is:
How far from a good choice would be applying time series forecasting to predict the future rental demand, which would clearly support the management of the cars relocation, instead of applying reinforcement learning?
Let's assume that the dataset also includes additional time series like number of rental opportunities lost by lack of vehicles, movement of cars between both locations due to customer preferences, dynamics of the fleet due to buy/sell operations, etc.
Is this a problem that you would address by applying a time series forecasting approach?
time-series predictive-modeling lstm reinforcement-learning forecasting
time-series predictive-modeling lstm reinforcement-learning forecasting
asked 3 mins ago
datarieldatariel
11
New contributor
datariel is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 3 mins ago
datarieldatariel
11
New contributor
datariel is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 3 mins ago
datarieldatariel
11
New contributor
datariel is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 3 mins ago
datarieldatariel
11
asked 3 mins ago
datarieldatariel
11
11
New contributor
datariel is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
datariel is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
datariel is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
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