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ML Time Series

BACKGROUND

The purpose of this project is to build a complete algorithm capable of predicting/forecasting time series. Testing time series will be provided for experiment purpose….. Target time series will be naturally divided into 3 groups regarding time zone.

The job is to build a testing bench where all implemented elements (as follows, i.e. pre-processing, models, Add-ins. method improvements, etc…) can be easily integrated, mixed-up the ones with the others and tested in order to find the best combination possible (2 measures will be taken into account – accuracy and frequency of occurrence of a good prediction).

- Valid forecast is a forecast that shows values @ +/- 0.3 % from the actual target.

- Valid frequency of occurrence of a good prediction is 80% of all dataset forecasts. If this floor is not reached, sub divide targets following discriminant features to eventually reach it…

Per target: Lead investigation on the statistical properties of time series to determine which sub group can be defined where the frequency floor is reached (basically wipe out the failing cases…).

This factor may show within correlated time series environment (some kind of advanced

This testing bench process will produce a standardised perf/robustness report per target enabling comparison between patchworked algos… (such as usual accuracy and performance measures, predicted values vs actual values and variation rate between both, distribution table of errors measures etc…).

• Pre-processing Modules

• Models Modules

• Pipelines Modules

• Benchmarking

• Implement a vanilla HMM for the benchmarking: Stock Market Prediction Using Hidden Markov Models (2012 paper).

• LSTM

• Deep Learning

ROAD MAP:

Pre-processing Modules 1 Week

• Designing in modular structure

• Implementation of indicators and reuse of previously implemented preprocessing techniques

models 1 Week

• Designing in modular structure

• Implementation of generic model interface

pipelines 1 Week

• Designing in modular structure

• Implementation of generic pipeline interface

benchmarking 1 Week

• Designing in modular structure

• Implementation of scoring functions and plotting

Deep Learning 1 Week

• Implementation of model and model specific preprocessing

HMM 1 Week

• Implementation of model and model specific preprocessing

LSTM 1 Week

• Implementation of model and model specific preprocessing

Evner: Machine Learning (ML), Python, MySQL

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Om arbejdsgiveren:
( 0 bedømmelser ) London, United Kingdom

Projekt ID: #20881539

Tildelt til:

ykocak

I've developed time-series analytics on various settings and I've previous experience with developing prediction systems on finance domain.

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26 freelancere byder i gennemsnit €2363 på dette job

tudiptechnology

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shivampanchal

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susanna2018

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IMdaystar

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marenchuk

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YuriyStepanovich

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murtaza1122

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cindcoder

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EmorphisIoT

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ryongshan

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Valuesolutions

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Software9981

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nehasoni2692

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dotablack

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engineeringexp

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healer818

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