Forecasting Currency Exchange Trend on USD/CAD
Bibliographic record
Abstract
Currency exchange rate forecast has been, and remains a challenging tasks. With unpredictable natural disasters, political instabilities, government policies, and many other factors, it becomes difficult to correctly forecast the currency exchange rate. Many researchers in the past have done great works on forecasting the exchange rate of the United State Dollar (USD)/Canadian Dollar (CAD) using statistical approach. Even the Fundamental approach of relying on macroeconomic factors of the two countries, such as GDP ratio, Import/Export, government revenue, etc. were considered at various points. But (while forecasting the USD/CAD exchange rate), none of the previous methods considered deeply the underlying market trends which forms the basics of Technical analysis. We have included various machine learning models and time sensitive indicators that directly aligns with the USD/CAD exchange rate movement so as to improve on this issue. These features will create a new dimension for researchers to predict and forecast the USD/CAD exchange rate. We have considered various types of models for predicting and forecasting the USD/CAD exchange rate, and realized that among all our models, Time Series models provides the best accuracy. While building our model, I relied of daily forex statistical data maintained by Tick Data Suite (TDS) and Investing.Com. All our macroeconomics data (Consumer Price Indices (CPI), Interest Rates, Imports, Exports, and Un Employment Rates) were automatically downloaded from the Federal Reserve Economic Data (FRED)’s portal via an API. Due to huge data size available to me, I used 90% of my data for training my models, and the remaining 10% for my testing. At the end, we were able to come up with good results from our models, of which Linear Regression, Bayesian Ridge Regression, and Random Forest Regressor (RFR) outperformed others based on accuracy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".