This work is a extensive interactive visualization as well as forecasting tool to forecast global monthly temperature. Choice of country and state (for which the forecasting is required) is menu driven and based upon dynamic subsetting of data. In addition, various graphs and parameters pertaining to the model builiding, comparison of predicted …

Traffic prediction and time-spent estimation in Buenos Aires' toll booths.

This repository contains accompanying code for some of my blog posts here:

Brazilian PIB (GDP) time series analysis.

Time series forecasting using different methods.

Identified the most appropriate Time-Series method to forecast drought in African countries, acting as a critical early warning for drought managements

To forecast the number of cases/applications a company will receive in next 3 months for 2 different segments

Time series analysis on NYC births data.

DSCI 524 Group 20: R package that analyzes stocks!

Forecasting monthly US unleaded gas prices using R tidyverts packages

Using MS Excel and R, accurately forecasted total core deposit data from a Richmond Bank. The Holt’s Linear Exponential Smoothing had the overall lowest “Quick and Dirty” MAPE (1.2%), the lowest overall Maximum MAPE (3.49%), and consistently more accurate projections for each of the forecast horizons. Overall, the Unaided, Holts Linear Exponenti…

ts-exponential-smoothing

Prediction of Rainfall in Sleman Regency with Exponential Smoothing Algorithm using R

Analyzed a 10-year USD/EUR exchange rate dataset using ARIMA and Exponential Smoothing forecasting models. Achieved a MAPE of approximately 4.68% and RMSE of about 0.0605 for both models.

This project aims to predict gold prices using various time series forecasting techniques. The dataset consists of monthly gold futures data over the last ten years. The primary methods used in this analysis include ARIMA, Error Trend Seasonal (ETS) models, and Exponential Smoothing techniques. The forecast horizon is set for the next two years.

A time series analysis of the monthly inflation rate of Germany 2008-2017

A comprehensive R project exploring time series analysis and forecasting techniques, including decomposition, exponential smoothing, ARIMA, dynamic regression, and hierarchical/grouped time series modeling, with clear visualizations and real-world datasets.

Time series forecasting analysis comparing Double Moving Average, Holt's Double Exponential Smoothing, and Linear Trend Model on 252 daily SPY ETF observations (Jan–Dec 2024). Selected optimal model using MSE — DMA: 7.73, Holt's: 18.44, Linear: 93.76 — generating a validated 5-day forward forecast indicating short-term market stabilisation.