Analyse de séries temporelles Jobs

I want to quantify how recent advances in artificial intelligence are influencing forecasts of French GDP. I already have access to long-run historical GDP time-series and would like you to work only with that dataset for now. The task is strictly quantitative: build and compare solid économétriques models that incorporate an IA variable (or proxy) and measure its marginal effect on growth projections. You will be free to choose among standard time-series techniques—VAR, ARDL, error-correction, whatever proves statistically sound—provided the approach is fully documented and reproducible in R, Python, or Stata. Along the way, please explain every transformation, stationarity check, and specification test so that the methodology can be defended in an academic sett...

I want an Android app that SIMULATES crash game behavior for PROBABILITY ANALYSIS purposes. Requirements: - Simulated crash rounds based on probability models - Streak & volatility analysis - Auto-generated signals (Low / Medium / Avoid) - Target outcome accuracy around 75%

Freelance R Expert Needed: Advanced Signal Processing for Maternal, Fetal & Neonatal Health We are seeking a senior R scientist with deep experience in audio / seismic data, wavelet methods, and Bayesian modeling to help demonstrate that R is just as good as Python at surfacing early physiological health markers. This role is for someone comfortable working below the noise floor—where the signal of interest is subtle, nonstationary, and embedded in the mechanics of living systems. What you’ll work on 1. Signal conditioning & noise removal • Design and evaluate signal conditioning pipelines for low-amplitude physiological data • Implement filtering strategies to remove mains contamination (50/60 Hz and harmonics), including: o Notch / comb filters o Adaptive ...

Overview I am seeking an experienced software developer to deliver Phase 1 of a research-grade macOS-native options analytics application. This phase is not about UI polish or trading automation. The goal is to de-risk the hardest parts early: Schwab API data integrity, trade/position grouping, and an analytics-ready architecture that can support advanced volatility, Greeks, and Monte Carlo analysis in later phases. This is a data and analytics foundation build, not a full application yet. Phase 1 Scope (Fixed & Explicit) In Scope 1. Native macOS application skeleton (Python preferred; alternatives must be justified) 2. Charles Schwab / thinkorswim Trader API integration A. Sandbox + production access B. Historical trades C. Open/closed positions D. Options chains, prices, IV...

I’ve compiled six months of raw sales data and now need a sharp statistical mind to transform those numbers into a reliable forecast. The objective is straightforward: build a data-driven model that predicts our future sales and highlights the key drivers behind those projections. You’ll start by cleaning and structuring the dataset, then apply time-series or other suitable predictive techniques in Python, R, or a comparable analytics environment. Please visualise the trends in clear charts (Matplotlib, Seaborn, ggplot2, or similar) and summarise your findings in a concise, non-technical narrative that management can act on immediately. Deliverables: • Cleaned and documented dataset • Reproducible analysis notebook or script • Forecast results with confiden...