Financial Time Series Forecasting
Usecases
- Stock price forecasting
- Index prediction
- Forex price prediction
- Commodity (oil, gold, etc) price prediction
- Bond price forecasting
- Volatility forecasting
- Cryptocurrency price forecasting
Patterns
- Price prediction and price movement (trend) prediction
- Researchers consider trend prediction
- Trend prediction becomes a classification problem - only up or down movements are taken into consideration. whereas up, down or neutral movements (3-class problem) also exist.
Time Aspects
- Period refers to the time period for training and testing
- Lag has the time length of the input vector (e.g. 30d means the input vector has a 30 day window)
- Horizon shows how far out into the future is predicted
Features
- Lagged stock returns
- Price data
- Turnover and number of trades.
- Daily closing prices
- Monthly and daily log-returns
- Price time series and emotional data from text posts for predicting the stock opening price of the next day
- Detecting the buy-sell pressure of movements
- GDP, Unemployment rate, Inventories, etc.
- Financial news
- Stock market data
- Volatility
- Technical indicators, Price data, News
- Twitter sentiment and stock prices
- Social media news, Index data
- Limit order book state
- Trades
- Buy/sell orders
- Order deletions
- Selected words in a news
- Weather conditions and various macroeconomic indicators
- Specific customer shipment patterns or the current competitive market situation
- A further example is the transformation of categorical variables into several binary values via so-called one-hot encoding trends or seasonal components
- When classifying the competitive market situation as “highly competitive”, “moderately competitive”, “not competitive” and the like
- When a particular weather situation coincides with a peak shipment date, nullifying or exacerbating the effect of the peak shipment date
Challenges
- Price disruptions, high volatility, bid-ask spread variations cause arbitrage opportunities across different platforms
What is the future direction for DL research for financial time series forecasting ?
- Response: NLP, semantics and text mining-based hybrid models ensembled with timeseries data might be more common in the near future.
Expert Aggregation for Financial Forecasting
- Aggregation with expert advice has the advantage of considering several forecasters instead of one, keeping the knowledge of each expert across time
- By weighting dynamically portfolios the mixture decreases the mean mixture excess risk, ensuring that on average the aggregation forecasting loss is close or better than the one of the best expert
- Stocks are then sorted according to their expert’s prediction, allowing to build two portfolios (stocks to be invested or shorted) for each expert.
- In a second step, the expert portfolios are aggregated based on the strategy returns, building an adaptive convex combination of the family of portfolios.
MACHINE LEARNING FOR FINANCIAL FORECASTING, PLANNING AND ANALYSIS: RECENT DEVELOPMENTS AND PITFALLS
- Fraud detection and financial forecasting. Planning and resource allocation
- Investments in research and development (R&D)
- Expansion of production capacity
- Financial obligations to debt holders or equity investors and tax authorities
- The time horizons considered for financial forecasts and plans usually range from one month to several years
- A practical example is to predict the sales of a product using input variables such as time of the year, price level, advertising expenditures and availability of competitor products.
- LSTM is also suitable for complex data sequences such as stock time series extracted from financial markets because it has internal memory, has capability of customization, and is free from gradient-related issues
- Since we make predictions only for one time step ahead at a time for an input time series, the LSTM architecture implemented here is the many-to-one type
- Dynamic Time Wrapping
- Common Periodicity Detection Algorithms
- Time domain: autocorrelation function
- Freq domain: Fisher’s test by Periodogram
- Short-term forecasting: predict the near future
- Long-term forecasting: predict the future with an extended period
- Extreme value forecasting: predict the extreme values
- Point or Probabilistic forecasting: predict point value or interval/probability distribution
Nbeats
DeepAR
TFT
Autoformer: Transformer with auto-correlation mechanism
FEDformer: frequency enhanced decomposed Transformer
Quatformer: Transformer with quaternions for periodic time series
Time-Series Works and Conferences
Dynamic Time Warping (DTW) variations
- FastDTW
- RobustDTW
Kstacked LSTM
Ref link
- What is Demand Forecasting? Importance and Benefits of Forecasting Customer Demand
- Scenario Analysis
- Scenario Analysis - Financial Portfolio
- Introduction to Factor Analysis in Python
- DeepTime: Using Deep Time-Index Meta-Learning to Improve Non-Stationary Time-Series Forecasting
- A time-series is a series of data measurements over time – a sequential collection of numerical data
- Non-Stationarity: When Time Series Changes Over Time
- Stationarity refers to time series data values that stay within a range, as well as regularity in the time series statistical patterns
- Meta-learning is a technique that aims to achieve the kind of quick learning exhibited by humans
- The inner learning loop learns very quickly from a small set of examples, called the support set.
- The outer learning loop ensures that the inner loop can perform this fast adaptation on new support sets. This is done by being trained on a query set - a set containing similar but distinct examples from the initial support set.
- Single-shot: Make the predictions all at once.
- Autoregressive: Make one prediction at a time and feed the output back to the model.
- The width (number of time steps) of the input and label windows.
- The time offset between them.
- Which features are used as inputs, labels, or both.
Forecasting Notes
Ref Link
- RNNs/CNNs are able to extract the most relevant features without manual engineering
- Forecasting applications (e.g. retail demand, electricity load, weather, finance, etc.)
- Bregman Volatility allows us to compute the optimal volatility of a sequence of forecasts
MQTransformer: Multi-Horizon Forecasts with Context-Dependent and Feedback-Aware Attention
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